def generate(histList, varName, varLow, varHigh, histSig="", weightSig=0.):
"""
Generate toy data
@param histList List of histograms
@param varname Variable name to use
@param varLow Lower edge of left bin
@param varHigh Upper edge of right bin
@param histSig Optional signal histogram
@param weightSig Optional signal weight
"""
testHist_pred = TH1D()
for (iHist, hist) in enumerate(histList):
if not iHist:
testHist_pred = hist.Clone("hPRED")
else:
testHist_pred.Add(hist)
testHist_pred_ds = TH1D("hPSDATA", "hPSDATA", testHist_pred.GetNbinsX(), testHist_pred.GetXaxis().GetBinLowEdge(1), testHist_pred.GetXaxis().GetBinUpEdge(testHist_pred.GetNbinsX()))
nEvents_pred = int(testHist_pred.Integral())
print "nEvents_pred = %d" % nEvents_pred
var = RooRealVar(varName, varName, varLow, varHigh)
dh_pred = RooDataHist("dhPSDATA", "dhPSDATA", RooArgList(var), RooFit.Import(testHist_pred))
ph_pred = RooHistPdf("phPSDATA", "phPSDATA", RooArgSet(var), dh_pred)
ds_pred = ph_pred.generate(RooArgSet(var), nEvents_pred)
testHist_pred_ds = ds_pred.fillHistogram(testHist_pred_ds, RooArgList(var))
return testHist_pred_ds
def performFitInLeptonAbsEta(data_histogram, signal_histogram, bkg1_histogram,
bkg2_histogram):
N_Data = data_histogram.Integral()
N_signal = signal_histogram.Integral()
N_bkg1 = bkg1_histogram.Integral()
N_bkg2 = bkg2_histogram.Integral()
leptonAbsEta = RooRealVar("leptonAbsEta", "leptonAbsEta", 0., 3.)
variables = RooArgList()
variables.add(leptonAbsEta)
variable_set = RooArgSet()
variable_set.add(leptonAbsEta)
lowerBound = 0
upperBound = N_Data * 2
data_RooDataHist = RooDataHist("data", "dataset with leptonAbsEta",
variables, data_histogram)
signal_RooDataHist = RooDataHist("rh_signal", "signal", variables,
signal_histogram)
bkg1_RooDataHist = RooDataHist("rh_bkg1", "bkg1", variables,
bkg1_histogram)
bkg2_RooDataHist = RooDataHist("rh_bkg2", "bkg2", variables,
bkg2_histogram)
signal_RooHistPdf = RooHistPdf("pdf_signal", "Signal PDF", variable_set,
signal_RooDataHist, 0)
signal_RooHistPdf = RooHistPdf("pdf_signal", "Signal PDF", variable_set,
signal_RooDataHist, 0)
def generate_hist(self, hist, varname, n):
_var = RooRealVar(varname, varname, hist.GetMean())
_dh = RooDataHist("dh", "dh", RooArgList(_var), hist)
_pdf = RooHistPdf("pdf", "pdf", RooArgSet(_var), _dh)
_ds = _pdf.generateBinned(RooArgSet(_var), n)
_h = _ds.createHistogram('hist', _var)
#_h.Draw()
#raw_input('press <enter> to continue...')
return copy.deepcopy(_h)
def generate_hist(self, hist, varname, n):
_var = RooRealVar(varname, varname, hist.GetMean())
_dh = RooDataHist("dh", "dh", RooArgList(_var), hist)
_pdf = RooHistPdf("pdf", "pdf", RooArgSet(_var), _dh)
_ds = _pdf.generateBinned(RooArgSet(_var), n)
_h = _ds.createHistogram('hist', _var)
#_h.Draw()
#raw_input('press <enter> to continue...')
return copy.deepcopy(_h)
def makeHistPdf(hist, ws, x):
theVars = RooArgList(x)
v = RooArgSet(x)
dataHist = RooDataHist(hist.GetName() + '_dh', 'dataHist', theVars, hist)
hpdf = RooHistPdf(hist.GetName() + '_pdf', 'hist pdf', v, dataHist)
getattr(ws, 'import')(hpdf)
return hpdf
def get_roofit_model( histograms, fit_boundaries, name = 'model' ):
data_label = 'data'
samples = sorted( histograms.keys() )
samples.remove( data_label )
roofit_histograms = {}
roofit_pdfs = {}
roofit_variables = {}
variables = RooArgList()
variable_set = RooArgSet()
fit_variable = RooRealVar( name , name, fit_boundaries[0], fit_boundaries[1] )
variables.add( fit_variable )
variable_set.add( fit_variable )
roofit_histograms[data_label] = RooDataHist( data_label,
data_label,
variables,
histograms[data_label] )
pdf_arglist = RooArgList()
variable_arglist = RooArgList()
N_total = histograms[data_label].Integral() * 2
N_min = 0
for sample in samples:
roofit_histogram = RooDataHist( sample, sample, variables, histograms[sample] )
roofit_histograms[sample] = roofit_histogram
roofit_pdf = RooHistPdf ( 'pdf' + sample, 'pdf' + sample, variable_set, roofit_histogram, 0 )
roofit_pdfs[sample] = roofit_pdf
roofit_variable = RooRealVar( sample, "number of " + sample + " events", histograms[sample].Integral(), N_min, N_total, "event" )
roofit_variables[sample] = roofit_variable
pdf_arglist.add( roofit_pdf )
variable_arglist.add( roofit_variable )
model = RooAddPdf( name, name, pdf_arglist, variable_arglist )
return model, roofit_histograms, fit_variable
def _make_underlying_model(self):
self.pdfs = {}
self.yields = {} # yields are plain floats
self.ryields = {} # keep track of roofit objects for memory management
nbins, xmin, xmax = self.plot.histos[0].GetBinning()
self.xvar = RooRealVar("x", "x", xmin, xmax)
self.xvar.setBins(nbins)
self.pdfs = {}
self.hists = []
pdfs = RooArgList()
yields = RooArgList()
for compname, comp in self.plot.histosDict.iteritems():
if comp.weighted.Integral() == 0:
continue
assert (isinstance(comp, Histogram))
hist = RooDataHist(compname, compname, RooArgList(self.xvar),
comp.weighted)
SetOwnership(hist, False)
# self.hists.append(hist)
pdf = RooHistPdf(compname, compname, RooArgSet(self.xvar), hist)
self.pdfs[compname] = pdf
# self.pdfs[compname].Print()
pdfs.add(pdf)
nevts = comp.Integral(xmin=xmin, xmax=xmax)
nmin = min(0, nevts * (1 - comp.uncertainty))
nmax = nevts * (1 + comp.uncertainty)
theyield = RooRealVar('n{}'.format(compname),
'n{}'.format(compname), nevts, nmin, nmax)
self.ryields[compname] = theyield
self.yields[compname] = nevts
yields.add(theyield)
self.underlying_model = RooAddPdf('model', 'model', pdfs, yields)
def fit(self, save_to, signal_name=None, fix_p3=False, fit_range=[300., 1200.], fit_strategy=1):
# Run a RooFit fit
# Create background PDF
p1 = RooRealVar('p1','p1',args.p1,0.,100.)
p2 = RooRealVar('p2','p2',args.p2,0.,60.)
p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
if args.fix_p3:
p3.setConstant()
background_pdf = RooGenericPdf('background_pdf','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(self.collision_energy,self.collision_energy,self.collision_energy),RooArgList(self.mjj_,p1,p2,p3))
background_pdf.Print()
data_integral = data_histogram.Integral(data_histogram.GetXaxis().FindBin(float(fit_range[0])),data_histogram.GetXaxis().FindBin(float(fit_range[1])))
background_norm = RooRealVar('background_norm','background_norm',data_integral,0.,1e+08)
background_norm.Print()
# Create signal PDF and fit model
if signal_name:
signal_pdf = RooHistPdf('signal_pdf', 'signal_pdf', RooArgSet(self.mjj_), self.signal_roohistograms_[signal_name])
signal_pdf.Print()
signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+05,1e+05)
signal_norm.Print()
model = RooAddPdf("model","s+b",RooArgList(background_pdf,signal_pdf),RooArgList(background_norm,signal_norm))
else:
model = RooAddPdf("model","b",RooArgList(background_pdf),RooArgList(background_norm))
# Run fit
res = model.fitTo(data_, RooFit.Save(kTRUE), RooFit.Strategy(fit_strategy))
# Save to workspace
self.workspace_ = RooWorkspace('w','workspace')
#getattr(w,'import')(background,ROOT.RooCmdArg())
getattr(self.workspace_,'import')(background_pdf,RooFit.Rename("background"))
getattr(self.workspace_,'import')(background_norm,ROOT.RooCmdArg())
getattr(self.workspace_,'import')(self.data_roohistogram_,RooFit.Rename("data_obs"))
getattr(self.workspace_, 'import')(model, RooFit.Rename("model"))
if signal_name:
getattr(self.workspace_,'import')(signal_roohistogram,RooFit.Rename("signal"))
getattr(self.workspace_,'import')(signal_pdf,RooFit.Rename("signal_pdf"))
getattr(self.workspace_,'import')(signal_norm,ROOT.RooCmdArg())
self.workspace_.Print()
self.workspace_.writeToFile(save_to)
if signal_name:
roofit_results[signal_name] = save_to
else:
roofit_results["background"] = save_to
def get_num_sig_bkg(hist_DataTemplate, hist_SignalTemplate,
hist_BackgdTemplate, fit_range_min, fit_range_max):
'''Given 3 input histograms (TH1F), and a fit range, this function finds
the amount of signal and background that sum up to the data histogram.
It does histogram fits.'''
# Find range of data template
data_min = hist_DataTemplate.GetXaxis().GetXmin()
data_max = hist_DataTemplate.GetXaxis().GetXmax()
# Create basic variables
x = RooRealVar("x", "x", data_min, data_max)
x.setBins(hist_DataTemplate.GetXaxis().GetNbins()) # Binned x values
nsig = RooRealVar("nsig", "number of signal events", 0,
hist_DataTemplate.Integral())
nbkg = RooRealVar("nbkg", "number of background events", 0,
hist_DataTemplate.Integral())
# Create RooDataHists from input TH1Fs
dh = RooDataHist("dh", "dh", RooArgList(x), hist_DataTemplate)
ds = RooDataHist("ds", "ds", RooArgList(x), hist_SignalTemplate)
db = RooDataHist("db", "db", RooArgList(x), hist_BackgdTemplate)
# Create Probability Distribution Functions from Monte Carlo
sigPDF = RooHistPdf("sigPDF", "sigPDF", RooArgSet(x), ds)
bkgPDF = RooHistPdf("bkgPDF", "bkgPDF", RooArgSet(x), db)
model = RooAddPdf("model", "(g1+g2)+a", RooArgList(bkgPDF, sigPDF),
RooArgList(nbkg, nsig))
# Find the edges of the bins that contain the fit range min/max
data_min = hist_DataTemplate.GetXaxis().GetBinLowEdge(
hist_DataTemplate.GetXaxis().FindFixBin(fit_range_min))
data_max = hist_DataTemplate.GetXaxis().GetBinUpEdge(
hist_DataTemplate.GetXaxis().FindFixBin(fit_range_max))
r = model.fitTo(dh, RooFit.Save(), RooFit.Minos(0),
RooFit.PrintEvalErrors(0), RooFit.Extended(),
RooFit.Range(data_min, data_max))
r.Print("v")
#print nsig.getVal(), nsig.getError(), nbkg.getVal(), nbkg.getError()
return [nsig.getVal(), nsig.getError(), nbkg.getVal(), nbkg.getError()]
def generate(histList, varName, varLow, varHigh, histSig="", weightSig=0.):
"""
Generate toy data
@param histList List of histograms
@param varname Variable name to use
@param varLow Lower edge of left bin
@param varHigh Upper edge of right bin
@param histSig Optional signal histogram
@param weightSig Optional signal weight
"""
testHist_pred = TH1D()
for (iHist, hist) in enumerate(histList):
if not iHist:
testHist_pred = hist.Clone("hPRED")
else:
testHist_pred.Add(hist)
testHist_pred_ds = TH1D(
"hPSDATA", "hPSDATA", testHist_pred.GetNbinsX(),
testHist_pred.GetXaxis().GetBinLowEdge(1),
testHist_pred.GetXaxis().GetBinUpEdge(testHist_pred.GetNbinsX()))
nEvents_pred = int(testHist_pred.Integral())
print "nEvents_pred = %d" % nEvents_pred
var = RooRealVar(varName, varName, varLow, varHigh)
dh_pred = RooDataHist("dhPSDATA", "dhPSDATA", RooArgList(var),
RooFit.Import(testHist_pred))
ph_pred = RooHistPdf("phPSDATA", "phPSDATA", RooArgSet(var), dh_pred)
ds_pred = ph_pred.generate(RooArgSet(var), nEvents_pred)
testHist_pred_ds = ds_pred.fillHistogram(testHist_pred_ds, RooArgList(var))
return testHist_pred_ds
def generate(histList,varName,varLow,varHigh,histSig="",weightSig=0.):
testHist_pred = TH1D()
for (iHist,hist) in enumerate(histList):
if not iHist:
testHist_pred = hist.Clone("hPRED")
else:
testHist_pred.Add(hist)
testHist_pred_ds = TH1D("hPSDATA","hPSDATA",testHist_pred.GetNbinsX(),testHist_pred.GetXaxis().GetBinLowEdge(1),testHist_pred.GetXaxis().GetBinUpEdge(testHist_pred.GetNbinsX()))
nEvents_pred = int(testHist_pred.Integral())
print "nEvents_pred = %d" % nEvents_pred
var = RooRealVar(varName,varName,varLow,varHigh)
dh_pred = RooDataHist("dhPSDATA","dhPSDATA",RooArgList(var),RooFit.Import(testHist_pred))
ph_pred = RooHistPdf("phPSDATA","phPSDATA",RooArgSet(var),dh_pred)
ds_pred = ph_pred.generate(RooArgSet(var),nEvents_pred)
testHist_pred_ds = ds_pred.fillHistogram(testHist_pred_ds,RooArgList(var))
return testHist_pred_ds
def makePDFs(Files, pdf_list, evntCount):
for (inputMass, inputFilename) in Files:
histo = load_histos(inputFilename)
evntCount.append(histo.Integral())
# could construct name and title from inputmass
if 'nom' in inputFilename:
myString = inputFilename.partition("230_")[2].partition(
"_result")[0] + str(inputMass)
else:
myString = inputFilename.partition("jes_")[2].partition(
"_result")[0] + str(inputMass)
# Convert the TH1F Histogram (data_hist) into a RooDataHist object
data_hist = RooDataHist(myString, myString, RooArgList(x), histo)
# Turn the RooDataHist object into a RooHistPdf object
pdf = RooHistPdf(myString, myString, RooArgSet(x), data_hist)
# both the data_hist and pdf must survive
pdf_list.append([data_hist, pdf])
def Hist2Pdf(self, hist, pdfName, ws, order=0):
if ws.pdf(pdfName):
return ws.pdf(pdfName)
try:
obs = [self.pars.varNames[x] for x in self.pars.var]
except AttributeError:
obs = self.pars.var
varList = RooArgList()
for v in obs:
varList.add(ws.var(v))
newHist = RooDataHist(pdfName + '_hist', pdfName + '_hist', varList,
hist)
thePdf = RooHistPdf(pdfName, pdfName, RooArgSet(varList), newHist,
order)
getattr(ws, 'import')(thePdf)
return ws.pdf(pdfName)
def _make_underlying_model(self):
self.pdfs = {}
self.yields = {} # yields are plain floats
self.ryields = {} # keep track of roofit objects for memory management
h = self._histos[0]
nbins = h.GetXaxis().GetNbins()
xmin = h.GetXaxis().GetXmin()
xmax = h.GetXaxis().GetXmax()
self.xvar = RooRealVar("x", "x", xmin, xmax)
self.xvar.setBins(nbins)
self.pdfs = {}
self.hists = []
pdfs = RooArgList()
yields = RooArgList()
for histo in self._histos:
if histo.Integral() == 0:
continue
compname = histo.GetName()
hist = RooDataHist(compname, compname, RooArgList(self.xvar),
histo)
SetOwnership(hist, False)
# self.hists.append(hist)
pdf = RooHistPdf(compname, compname, RooArgSet(self.xvar), hist)
self.pdfs[compname] = pdf
# self.pdfs[compname].Print()
pdfs.add(pdf)
nevts = histo.Integral()
uncertainty = self._uncertainty
nmin = min(0, nevts * (1 - uncertainty))
nmax = nevts * (1 + uncertainty)
theyield = RooRealVar('n{}'.format(compname),
'n{}'.format(compname), nevts, nmin, nmax)
self.ryields[compname] = theyield
self.yields[compname] = nevts
yields.add(theyield)
self.underlying_model = RooAddPdf('model', 'model', pdfs, yields)
def broadenspec(ms, E_0):
spec = decayspec(ms, E_0)
smear = core(E_0_center)
newspec = TH1D("", "", spec.GetNbinsX(), -17.5, 17.5)
for i in range(1, spec.GetNbinsX() + 1):
newspec.SetBinContent(i, spec.GetBinContent(i))
#x = RooRealVar("x","x",-30+E_0_center, 5+E_0_center)
x = RooRealVar("x", "x", -17.5, 17.5)
data = RooDataHist("", "", RooArgList(x), newspec)
specpdf = RooHistPdf("", "", RooArgSet(x), data)
#y = RooRealVar("y","y",-30, 5)
x.setBins(10000)
smearpdf = RooFit.bindPdf(smear, x)
fft = RooFFTConvPdf("tt", "tt", x, specpdf, smearpdf)
#fft.setShift(0, -18574)
#c1 = TCanvas()
#frame = x.frame()
#fft.plotOn(frame)
#frame.Draw()
tf = fft.asTF(RooArgList(x))
tf.SetNpx(10000)
rtf = tf.Clone()
return rtf
# Not to be included in the Moment Morphing (just a check)
file3 = R.TFile(f3)
hist3 = file3.Get('my_model_hist')
hist3.SetDirectory(0)
hist3_events = hist3.Integral()
# Setup Workspace and frame
w = R.RooWorkspace('w')
x = w.factory('x[70,230]')
x.setBins(32)
frame = x.frame()
# Z = 125 GeV
data_hist1 = RooDataHist('dhist1', 'dhist1', RooArgList(x), hist1)
pdf1 = RooHistPdf('phist1', 'phist1', RooArgSet(x), data_hist1)
pdf1.plotOn(frame, RooFit.LineColor(R.kGreen),
RooFit.Normalization(hist1_events))
# Z = 175 GeV
data_hist2 = RooDataHist('dhist2', 'dhist2', RooArgList(x), hist2)
pdf2 = RooHistPdf('phist2', 'phist2', RooArgSet(x), data_hist2)
pdf2.plotOn(frame, RooFit.LineColor(R.kGreen),
RooFit.Normalization(hist2_events))
# Not to be included in the Moment Morphing (just a check)
data_hist3 = RooDataHist('dhist3', 'dhist3', RooArgList(x), hist3)
pdf3 = RooHistPdf('phist3', 'phist3', RooArgSet(x), data_hist3)
pdf3.plotOn(frame, RooFit.LineColor(R.kBlue),
RooFit.Normalization(hist3_events))
data_hist3.plotOn(frame)
# Create a RooArgList to store pdfs and fill it
mmgMassFunc = w.factory('''FormulaVar::mmgMassFunc(
"((mmgMass - mmgMode) / mmgScale + mmgMode) - fPho*phoScale/100.",
{mmgMass, mmgMode[91.2,70,110], mmgScale[1.,0.1,5], fPho, phoScale}
)''')
mmgMode = w.var('mmgMode')
mmgScale = w.var('mmgScale')
## Get the nominal model for translation, use rho=2
model = RooKeysPdf('model', 'model', mmgMass, mmgData, RooKeysPdf.NoMirror, 2)
## Sample nominal model to binned data, no Poisson fluctuation
modelHist = model.createHistogram('mmgMass', 10000)
modelDH = RooDataHist('modelDH', 'modelDH', RooArgList(mmgMass), modelHist)
## Use the sampled model to build a transformed model, interpolation order=2
tmodel = RooHistPdf('tmodel', 'tmodel', RooArgList(mmgMassFunc),
RooArgList(mmgMass), modelDH, 2)
## Find the mode of the tmodel.
mmgMode.setVal(modelHist.GetBinCenter(modelHist.GetMaximumBin()))
mmgMode.setConstant(True)
fPho.setConstant(True)
## Fit the photon scale
phoScale.setConstant(False)
mmgScale.setConstant(True)
fac, s = 1, 0
mmgMass.SetTitle('scaledMmgMass3(%f, mmgMass, mmMass)' % fac)
phoPtRangeMod = (phoPtRange[0] * fac, phoPtRange[1] * fac)
full_ch.Add("/mnt/home/kklimaszewski/LHCb/Bs2JpsieePhi/M_Bs_Fits/mc/DVNtuples_Bs2JpsieePhi_13154001_MCfull_R14ac_TupleBsDetached_PIDCorr_S21_NewSel_tupleB.root")
# Run selection
ch = full_ch.CopyTree("sigmam>0. && sigmam<250. && time>0.3 && time<14. && sigmat<0.12 && BDT_response_NewSel>0.2 && eminus_bremmult==0 && eplus_bremmult==0")
#fit.mass.setBins(_comp['config']['bins'])
# Create hist PDF
data = RooDataSet(
'data_set',
'',
RooArgSet(x),
RooFit.Import(ch)
)
hist = data.binnedClone()
p1 = RooHistPdf(
'histpdf',
'',
RooArgSet(x),
hist,
2 # Order of interpolation function
)
#p2 = RooGaussian(
p2 = RooGaussModel(
"comp_2",
"",
x,
mean,
sigma
)
#pdf = RooNumConvPdf("pdf", 'convolution', x, p1, p2)
x.setBins(10000,"fft") ;
pdf = RooFFTConvPdf("pdf", 'convolution', x, p2, p1)
cf_p = TCanvas('cf_p', 'fit, parametrized')
cf_p_frame = x.frame()
data.plotOn(cf_p_frame)
model.plotOn(cf_p_frame)
cf_p_frame.Draw()
######### model definition, template
nsig_tpl = 10000
nbgd_tpl = 1000000
exp_sig_data = sig.generate(RooArgSet(x), nsig_tpl)
exp_sig_hist = exp_sig_data.binnedClone()
tsig = RooHistPdf('tsig', 'tsig', RooArgSet(x), exp_sig_hist)
exp_bgd_data = bgd.generate(RooArgSet(x), nbgd_tpl)
exp_bgd_hist = exp_bgd_data.binnedClone()
tbgd = RooHistPdf('tbgd', 'tbgd', RooArgSet(x), exp_bgd_hist)
# modelsig = RooAddPdf("modelsig", "modelsig", RooArgList(tsig), RooArgList(vysig))
# tmodel = RooAddPdf("tmodel", "tmodel", RooArgList(tsig), RooArgList(vysig))
tmodel = RooAddPdf("tmodel", "tmodel", RooArgList(tsig, tbgd),
RooArgList(vysig, vybgd))
######### data generation, template
tdata = tmodel.generate(RooArgSet(x), ndata)
######### fit, template
def shapeCards(datahistosFile, histosFile, signalFile, signalSample, hist,
signalMass, minMass, maxMass, outputName, outputFileTheta):
"""function to run Roofit and save workspace for RooStats"""
warnings.filterwarnings(
action='ignore',
category=RuntimeWarning,
message='.*class stack<RooAbsArg\*,deque<RooAbsArg\*> >')
############################################################# DATA
#hData = histosFile.Get('massAve_deltaEtaDijet_QCDPtAll')
#hData = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
dataFile = TFile(datahistosFile)
hData = dataFile.Get(hist + '_DATA')
hData.Rebin(args.reBin)
#hData = hData.Rebin( len( boostedMassAveBins )-1, hData.GetName(), boostedMassAveBins )
#hData = histosFile.Get(hist+'_QCDPtAll_A')
#hData.Add(htmpSignal)
#hData.Scale(1/hData.Integral())
#maxMass = boostedMassAveBins[ hData.FindLastBinAbove( 0, 1) ]
#minMass = signalMass-30
#maxMass = signalMass+30
massAve = RooRealVar('massAve', 'massAve', minMass, maxMass)
#massAveData = RooRealVar( 'massAveData', 'massAveData', minMass, maxMass )
rooDataHist = RooDataHist('rooDatahist', 'rooDatahist',
RooArgList(massAve),
hData) # if isData else hPseudo )
rooDataHist.Print()
############################################################################################
####################### Signal
if 'gaus' in args.job:
hSignal = TH1F('massAve_RPVStop', 'massAve_RPVStop',
maxMass / args.reBin, minMass, maxMass)
for q in range(hSignal.GetNbinsX() + 1):
gausEval = signalFile.Eval(hSignal.GetXaxis().GetBinCenter(q))
hSignal.SetBinContent(q, gausEval)
#meanSig = RooRealVar( 'meanSig', 'mean of signal', sigGaus.GetParameter( 1 ) )
#sigmaSig = RooRealVar( 'sigmaSig', 'sigma of signal', sigGaus.GetParameter( 2 ) )
#signalPdf = RooGaussian( 'signal', 'signal', massAve, meanSig, sigmaSig )
#signalPdf.Print()
else:
signalHistosFile = TFile(signalFile)
hSignal = signalHistosFile.Get(hist + '_' + signalSample)
hSignal.Rebin(args.reBin)
hSignal.Scale(twoProngSF)
signalXS = search(dictXS, 'RPVStopStopToJets_UDD312_M-' + str(signalMass))
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(massAve),
hSignal)
sigAcc = rooSigHist.sumEntries(
) #round(hSignal.Integral( hSignal.GetXaxis().FindBin( minMass ), hSignal.GetXaxis().FindBin( maxMass )), 2)
rooSigHist.Print()
#signal = RooHistPdf('signal','signal',RooArgSet(massAve),rooSigHist)
#signal.Print()
#signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+04,1e+04)
#if args.fitBonly: signal_norm.setConstant()
#signal_norm.Print()
#####################################################################
hSigSyst = signalUnc(hSignal, signalMass)
hSigSystDataHist = {}
if args.jesUnc:
hSigSystDataHist['JESUp'] = RooDataHist('hSignalJESUp', 'hSignalJESUp',
RooArgList(massAve),
hSigSyst['JESUp'])
hSigSystDataHist['JESDown'] = RooDataHist('hSignalJESDown',
'hSignalJESDown',
RooArgList(massAve),
hSigSyst['JESDown'])
if args.jerUnc:
hSigSystDataHist['JERUp'] = RooDataHist('hSignalJERUp', 'hSignalJERUp',
RooArgList(massAve),
hSigSyst['JERUp'])
hSigSystDataHist['JERDown'] = RooDataHist('hSignalJERDown',
'hSignalJERDown',
RooArgList(massAve),
hSigSyst['JERDown'])
#################################### Background
if args.altBkg:
newBkgHistoFile = datahistosFile.replace('DATA', 'DATA_ABCDBkg')
newBkgFile = TFile(newBkgHistoFile)
htmpBkg = newBkgFile.Get(
'massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
if (htmpBkg.GetBinWidth(1) != args.reBin):
print '|----- Bin size in DATA_C histogram is different than rest.'
sys.exit(0)
else:
htmpBkg = dataFile.Get(
'massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
htmpBkg.Rebin(args.reBin)
#hBkg = histosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_QCDPtAll_ABCDProj')
#hBkg = histosFile.Get(hist+'_QCDPtAll_BCD')
#htmpBkg = htmpBkg.Rebin( len( boostedMassAveBins )-1, htmpBkg.GetName(), boostedMassAveBins )
hBkg = htmpBkg.Clone()
hBkg.Reset()
for ibin in range(htmpBkg.GetNbinsX()):
binCont = htmpBkg.GetBinContent(ibin)
binErr = htmpBkg.GetBinError(ibin)
if binCont == 0:
hBkg.SetBinContent(ibin, 0)
hBkg.SetBinError(ibin, 1.8)
else:
hBkg.SetBinContent(ibin, binCont)
hBkg.SetBinError(ibin, binErr)
#hBkg.Scale(1/hBkg.Integral())
hPseudo = createPseudoExperiment(hBkg, bkgAcc)
###### Adding statistical uncertanty
hBkgStatUncUp = hBkg.Clone()
hBkgStatUncUp.Reset()
hBkgStatUncDown = hBkg.Clone()
hBkgStatUncDown.Reset()
for i in range(hBkg.GetNbinsX() + 1):
cont = hBkg.GetBinContent(i)
contErr = hBkg.GetBinError(i)
hBkgStatUncUp.SetBinContent(i, cont + (1 * contErr))
hBkgStatUncDown.SetBinContent(i, cont - (1 * contErr))
hBkgStatUncUpDataHist = RooDataHist('hBkgStatUncUp', 'hBkgStatUncUp',
RooArgList(massAve), hBkgStatUncUp)
hBkgStatUncDownDataHist = RooDataHist('hBkgStatUncDown', 'hBkgStatUncDown',
RooArgList(massAve), hBkgStatUncDown)
if 'template' in args.job:
rooBkgHist = RooDataHist('rooBkgHist', 'rooBkgHist',
RooArgList(massAve), hBkg)
bkgAcc = rooBkgHist.sumEntries()
rooBkgHist.Print()
background = RooHistPdf('background', 'background', RooArgSet(massAve),
rooBkgHist)
background.Print()
else:
massAveBkg = RooRealVar('massAveBkg', 'massAveBkg', minMass, maxMass)
p1 = RooRealVar('p1', 'p1', 1, 0., 100.)
p2 = RooRealVar('p2', 'p2', 1, 0., 60.)
p3 = RooRealVar('p3', 'p3', 1, -10., 10.)
bkgAcc = round(
hBkg.Integral(hBkg.GetXaxis().FindBin(minMass),
hBkg.GetXaxis().FindBin(maxMass)), 2)
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(1300, 1300, 1300), RooArgList(massAveBkg, p1, p2, p3))
background.Print()
hBkgSyst = {}
hBkgSystDataHist = {}
if args.bkgUnc:
print ' |---> Adding bkg unc'
hBkgSyst['BkgUncUp'] = hBkg.Clone()
hBkgSyst['BkgUncDown'] = hBkg.Clone()
for key in hBkgSyst:
hBkgSyst[key].Reset()
hBkgSyst[key].SetName(hBkgSyst[key].GetName() + '_' + key)
for q in range(0, hBkg.GetNbinsX()):
binCont = hBkg.GetBinContent(q)
bkgUncUp = 1. + (args.bkgUncValue / 100.)
hBkgSyst['BkgUncUp'].SetBinContent(q, binCont * bkgUncUp)
bkgUncDown = 1. - (args.bkgUncValue / 100.)
hBkgSyst['BkgUncDown'].SetBinContent(q, binCont * bkgUncDown)
hBkgSystDataHist['BkgUncUp'] = RooDataHist('hBkgBkgUncUp',
'hBkgBkgUncUp',
RooArgList(massAve),
hBkgSyst['BkgUncUp'])
hBkgSystDataHist['BkgUncDown'] = RooDataHist('hBkgBkgUncDown',
'hBkgBkgUncDown',
RooArgList(massAve),
hBkgSyst['BkgUncDown'])
############################################################################################
#model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
#res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(0))
#res.Print()
############################ Create Workspace
myWS = RooWorkspace("myWS")
getattr(myWS, 'import')(rooBkgHist, RooFit.Rename("background"))
#getattr(myWS,'import')(background,RooFit.Rename("background"))
#getattr(myWS,'import')(signal_norm)
#getattr(myWS,'import')(background_norm)
'''
if 'gaus' in args.job:
getattr(myWS,'import')(signalPdf,RooFit.Rename("signal"))
if args.jesUnc:
getattr(myWS,'import')(signalPdfJESUp,RooFit.Rename("signal__JESUp"))
getattr(myWS,'import')(signalPdfJESDown,RooFit.Rename("signal__JESDown"))
else:
'''
getattr(myWS, 'import')(rooSigHist, RooFit.Rename("signal"))
if args.jesUnc:
getattr(myWS, 'import')(hSigSystDataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(myWS, 'import')(hSigSystDataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if args.jerUnc:
getattr(myWS, 'import')(hSigSystDataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(myWS, 'import')(hSigSystDataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.bkgUnc:
getattr(myWS, 'import')(hBkgSystDataHist['BkgUncUp'],
RooFit.Rename("background__BkgUncUp"))
getattr(myWS, 'import')(hBkgSystDataHist['BkgUncDown'],
RooFit.Rename("background__BkgUncDown"))
getattr(myWS, 'import')(hBkgStatUncUpDataHist,
RooFit.Rename("background__BkgStatUncUp"))
getattr(myWS, 'import')(hBkgStatUncDownDataHist,
RooFit.Rename("background__BkgStatUncDown"))
getattr(myWS, 'import')(rooDataHist, RooFit.Rename("data_obs"))
myWS.Print()
outputRootFile = currentDir + '/Rootfiles/workspace_' + outputName + '.root'
myWS.writeToFile(outputRootFile, True)
print ' |----> Workspace created in root file:\n', outputRootFile
'''
c1 = TCanvas('c1', 'c1', 10, 10, 750, 500 )
# c1.SetLogy()
xframe = myWS.var("massAve").frame()
signalPdf.plotOn( xframe )
xframe.Draw()
c1.SaveAs('test.png')
del c1
'''
############################################################################################
######################### write a datacard
dataCardName = currentDir + '/Datacards/datacard_' + outputName + '.txt'
datacard = open(dataCardName, 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc or args.jerUnc or args.lumiUnc or args.bkgUnc or args.unc:
datacard.write('shapes * * ' + outputRootFile +
' myWS:$PROCESS myWS:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write("shapes * * " + outputRootFile + " myWS:$PROCESS \n")
datacard.write('---------------\n')
datacard.write('bin ' + signalSample + '\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin ' + signalSample + ' ' +
signalSample + '\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
#datacard.write('rate -1 -1\n')
datacard.write('rate ' + str(sigAcc) + ' ' + str(bkgAcc) +
'\n')
datacard.write('------------------------------\n')
if args.lumiUnc:
datacard.write('lumi lnN %f -\n' % (lumiValue))
if args.puUnc: datacard.write('pu lnN %f -\n' % (puValue))
if args.jesUnc: datacard.write('JES shape 1 -\n')
if args.jerUnc: datacard.write('JER shape 1 -\n')
#flat parameters --- flat prior
#if args.bkgUnc: datacard.write('BkgUnc shape - '+str( round( 1/ (args.bkgUncValue/34.1), 2 ) )+'\n')
if args.bkgUnc: datacard.write('BkgUnc shape - 1 \n')
#NcombineUnc = ( 1 / TMath.Sqrt( args.bkgUncValue / 100. ) ) - 1
#datacard.write('background_norm gmN '+str(int(round(NcombineUnc)))+' - '+str( round(bkgAcc/NcombineUnc,2) )+'\n')
#datacard.write('p1 flatParam\n')
datacard.write('BkgStatUnc shape - 1 \n')
datacard.close()
print ' |----> Datacard created:\n', dataCardName
############################################################################################
########## Theta
if args.theta:
print ' |----> Creating Theta file\n', outputFileTheta
outFile = TFile(outputFileTheta, 'update')
tmpName = 'rpvstopjj' + str(signalMass)
hSignal.SetName('massAve__' + tmpName)
hSignal.Write()
hSigSyst['JESDown'].SetName('massAve__' + tmpName + '__jes__down')
hSigSyst['JESDown'].Write()
hSigSyst['JESUp'].SetName('massAve__' + tmpName + '__jes__up')
hSigSyst['JESUp'].Write()
hSigSyst['JERDown'].SetName('massAve__' + tmpName + '__jer__down')
hSigSyst['JERDown'].Write()
hSigSyst['JERUp'].SetName('massAve__' + tmpName + '__jer__up')
hSigSyst['JERUp'].Write()
if (signalMass == 100): #or (signalMass == 170):
hBkg.SetName('massAve__background')
hBkg.Write()
hBkgSyst['BkgUncDown'].SetName('massAve__background__unc__down')
hBkgSyst['BkgUncDown'].Write()
hBkgSyst['BkgUncUp'].SetName('massAve__background__unc__up')
hBkgSyst['BkgUncUp'].Write()
hData.SetName('massAve__DATA')
hData.Write()
outFile.Close()
def main(options,args):
from ROOT import gSystem, gROOT, gStyle
gROOT.SetBatch()
gSystem.Load("libRooFitCore")
if options.doWebPage:
from lip.Tools.rootutils import loadToolsLib, apply_modifs
loadToolsLib()
from ROOT import TFile, RooFit, RooArgSet, RooDataHist, RooKeysPdf, RooHistPdf, TCanvas, TLegend, TLatex, TArrow, TPaveText, RooAddPdf, RooArgList
from ROOT import kWhite, kBlue, kOpenSquare
if options.doWebPage:
from ROOT import HtmlHelper, HtmlTag, HtmlTable, HtmlPlot
rootglobestyle.setTDRStyle()
gStyle.SetMarkerSize(1.5)
gStyle.SetTitleYOffset(1.5)
gStyle.SetPadLeftMargin(0.16)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.13)
gStyle.SetLabelFont(42,"XYZ")
gStyle.SetLabelOffset(0.007, "XYZ")
gStyle.SetLabelSize(0.05,"XYZ")
gStyle.SetTitleSize(0.06,"XYZ")
gStyle.SetTitleXOffset(0.9)
gStyle.SetTitleYOffset(1.24)
gStyle.SetTitleFont(42,"XYZ")
##
## Read files
##
options.outdir = "%s_m%1.0f" % ( options.outdir, options.mH )
if options.fp:
options.outdir += "_fp"
ncat=options.ncat
cats=options.cats
if cats is "":
categories =[ "_cat%d" % i for i in range(0,ncat) ]
else:
categories =[ "_cat%s" % i for i in cats.split(",") ]
if options.mva:
clables = { "_cat0" : ("MVA > 0.89",""),
"_cat1" : ("0.74 #leq MVA","MVA < 0.89"),
"_cat2" : ("0.545 #leq MVA","MVA < 0.74"),
"_cat3" : ("0.05 #leq MVA","MVA < 0.545"),
"_cat4" : ("Di-jet","Tagged"),
"_cat5" : ("Di-jet","Tagged"),
"_combcat" : ("All Classes","Combined")
}
else:
clables = { "_cat0" : ("max(|#eta|<1.5","min(R_{9})>0.94"),
"_cat1" : ("max(|#eta|<1.5","min(R_{9})<0.94"),
"_cat2" : ("max(|#eta|>1.5","min(R_{9})>0.94"),
"_cat3" : ("max(|#eta|>1.5","min(R_{9})<0.94"),
"_cat4" : ("Di-jet","Tagged"),
"_cat5" : ("Di-jet","Tagged"),
"_combcat" : ("All Classes","Combined")
}
helper = Helper()
fin = TFile.Open(options.infile)
helper.files.append(fin)
ws = fin.Get("cms_hgg_workspace")
mass = ws.var("CMS_hgg_mass")
mass.SetTitle("m_{#gamma#gamma}");
mass.setUnit("GeV");
mass.setRange(100.,150.)
mass.setBins(100,"plot")
mass.setBins(5000)
print ws
aset = RooArgSet(mass)
helper.objs.append( mass )
helper.objs.append( aset )
fitopt = ( RooFit.Minimizer("Minuit2", ""), RooFit.Minos(False), RooFit.SumW2Error(False), RooFit.NumCPU(8) )
if not options.binned and not options.refit:
finpdf = TFile.Open(options.infilepdf)
helper.files.append(finpdf)
wspdf = finpdf.Get("wsig")
else:
wspdf = ws
for c in categories:
processes = [ "ggh", "vbf", "wzh" ]
if options.fp:
processes = [ "vbf", "wzh" ]
### elif clables[c][0] == "Di-jet":
### processes = [ "vbf", "ggh" ]
dsname = "sig_mass_m%1.0f%s" % (options.mH,c)
print dsname
print ws
ds = ws.data( "sig_%s_mass_m%1.0f%s" % (processes[0],options.mH,c) ).Clone(dsname)
for proc in processes[1:]:
ds.append( ws.data( "sig_%s_mass_m%1.0f%s" % (proc,options.mH,c) ) )
helper.dsets.append( ds )
if options.binned:
binned_ds = RooDataHist( "binned_%s" % dsname,"binned_%s" % dsname,aset, ds)
pdf = RooKeysPdf( "pdf_%s_%s" % (dsname, f), "pdf_%s" % dsname, mass, ds )
plot_pdf = RooHistPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, aset, plot_ds )
helper.add( binned_ds, binned_ds.GetName() )
else:
if options.refit:
if options.refitall and clables[c][0] != "Di-jet":
rpdfs = []
for proc in processes:
for ngaus in range(1,4):
pp = build_pdf(ws,"%s_%s" % (c,proc),ngaus,ngaus==3 )
pp.fitTo( ws.data( "sig_%s_mass_m%1.0f%s" % (proc,options.mH,c)), RooFit.Strategy(0), *fitopt )
rpdfs.append(pp)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c, RooArgList(*tuple(rpdfs) ))
else:
if options.refitall and clables[c][0] == "Di-jet":
for ngaus in range(1,5):
pdf = build_pdf(ws,c,ngaus,ngaus==5)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt )
else:
for ngaus in range(1,4):
pdf = build_pdf(ws,c,ngaus,ngaus==3)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt )
else:
pdfs = (wspdf.pdf( "hggpdfrel%s_%s" % (c, p)) for p in processes )
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c, RooArgList(*pdfs ))
helper.add(pdf,pdf.GetName())
plot_pdf = pdf.Clone("pdf_%s" % dsname)
plot_ds = RooDataHist( "plot_%s" % dsname,"plot_%s" % dsname, aset, "plot")
plot_ds.add( ds )
cdf = pdf.createCdf(aset)
hmin, hmax, hm = get_FWHM( mass, pdf, cdf, options.mH-10., options.mH+10. )
wmin, wmax = get_eff_sigma( mass, pdf, cdf, options.mH-10., options.mH+10. )
### hmin, hmax, hm = get_FWHM( points )
helper.add( plot_ds, plot_ds.GetName() )
helper.add( plot_pdf, plot_pdf.GetName() )
helper.add( (wmin,wmax), "eff_sigma%s" % c )
helper.add( (hmin, hmax, hm), "FWHM%s" % c )
helper.add( ds.sumEntries(), "sumEntries%s" %c ) # signal model integral
# data integral for PAS tables
data = ws.data( "data_mass%s"%c)
helper.add( data.sumEntries("CMS_hgg_mass>=%1.4f && CMS_hgg_mass<=%1.4f"%(options.mH-10.,options.mH+10.)),"data_sumEntries%s"%c)
del cdf
del pdf
dsname = "sig_mass_m%1.0f_combcat" % options.mH
print dsname
combined_ds = helper.dsets[0].Clone(dsname)
for d in helper.dsets[1:]:
combined_ds.append(d)
if options.binned:
binned_ds = RooDataHist( "binned_%s" % dsname,"binned_%s" % dsname,aset, combined_ds)
pdf = RooKeysPdf( "pdf_%s" % (dsname), "pdf_%s" % dsname, mass, combined_ds )
plot_pdf = RooHistPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, aset, plot_ds )
helper.add( binned_ds, binned_ds.GetName() )
else:
#### pdf = build_pdf(ws,"_combcat")
#### pdf.fitTo(combined_ds, RooFit.Strategy(0), *fitopt )
#### plot_pdf = pdf.Clone( "pdf_%s" % dsname )
pdf = RooAddPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, RooArgList( *(helper.histos["hggpdfrel%s" % c] for c in categories) ) )
plot_pdf = pdf
cdf = pdf.createCdf(aset)
plot_ds = RooDataHist( "plot_%s" % dsname,"plot_%s" % dsname, aset, "plot")
plot_ds.add( combined_ds )
wmin, wmax = get_eff_sigma( mass, pdf, cdf, options.mH-10., options.mH+10. )
hmin, hmax, hm = get_FWHM( mass, pdf, cdf, options.mH-10., options.mH+10. )
helper.add( plot_ds, plot_ds.GetName() )
helper.add( plot_pdf, plot_pdf.GetName() )
helper.add( (wmin,wmax), "eff_sigma_combcat" )
helper.add( (hmin, hmax, hm), "FWHM_combcat" )
helper.add( plot_ds.sumEntries(), "sumEntries_combcat" )
mass.setRange("higgsrange",options.mH-25.,options.mH+15.);
del cdf
del pdf
del helper.dsets
### label = TLatex(0.1812081,0.8618881,"#scale[0.8]{#splitline{CMS preliminary}{Simulation}}")
label = TLatex(0.7,0.86,"#scale[0.65]{#splitline{CMS preliminary}{Simulation}}")
label.SetNDC(1)
##
## Make web page with plots
##
if options.doWebPage:
hth = HtmlHelper(options.outdir)
hth.navbar().cell( HtmlTag("a") ).firstChild().txt("..").set("href","../?C=M;O=D")
hth.navbar().cell( HtmlTag("a") ).firstChild().txt("home").set("href","./")
tab = hth.body().add( HtmlTable() )
ip = 0
for c in ["_combcat"]+categories:
### for c in categories:
if options.doWebPage and ip % 4 == 0:
row = tab.row()
ip = ip + 1
dsname = "sig_mass_m%1.0f%s" % (options.mH,c)
canv = TCanvas(dsname,dsname,600,600)
helper.objs.append(canv)
### leg = TLegend(0.4345638,0.6835664,0.9362416,0.9178322)
leg = TLegend(0.2,0.96,0.5,0.55)
#apply_modifs( leg, [("SetLineColor",kWhite),("SetFillColor",kWhite),("SetFillStyle",0),("SetLineStyle",0)] )
hplotcompint = mass.frame(RooFit.Bins(250),RooFit.Range("higgsrange"))
helper.objs.append(hplotcompint)
helper.objs.append(leg)
plot_ds =helper.histos["plot_%s" % dsname ]
plot_pdf =helper.histos["pdf_%s" % dsname ]
wmin,wmax = helper.histos["eff_sigma%s" % c ]
hmin, hmax, hm = helper.histos["FWHM%s" % c ]
print hmin, hmax, hm
style = ( RooFit.LineColor(kBlue), RooFit.LineWidth(2), RooFit.FillStyle(0) )
style_seff = ( RooFit.LineWidth(2), RooFit.FillStyle(1001), RooFit.VLines(), RooFit.LineColor(15), )
style_ds = ( RooFit.MarkerStyle(kOpenSquare), )
plot_ds.plotOn(hplotcompint,RooFit.Invisible())
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range(wmin,wmax), RooFit.FillColor(19),
RooFit.DrawOption("F"), *style_seff)
seffleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range(wmin,wmax), RooFit.LineColor(15), *style_seff)
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range("higgsrange"),*style)
pdfleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
plot_ds.plotOn(hplotcompint,*style_ds)
pointsleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
iob = int( hplotcompint.numItems() - 1 )
leg.AddEntry( pointsleg, "Simulation", "pe" )
leg.AddEntry( pdfleg, "Parametric model", "l" )
leg.AddEntry( seffleg, "#sigma_{eff} = %1.2f GeV " % ( 0.5*(wmax-wmin) ), "fl" )
clabel = TLatex(0.74,0.65,"#scale[0.65]{#splitline{%s}{%s}}" % clables[c])
clabel.SetNDC(1)
helper.objs.append(clabel)
hm = hplotcompint.GetMaximum()*0.5*0.9
### hm = pdfleg.GetMaximum()*0.5
fwhmarrow = TArrow(hmin,hm,hmax,hm)
fwhmarrow.SetArrowSize(0.03)
helper.objs.append(fwhmarrow)
fwhmlabel = TPaveText(0.20,0.58,0.56,0.48,"brNDC")
fwhmlabel.SetFillStyle(0)
fwhmlabel.SetLineColor(kWhite)
reducedFWHM = (hmax-hmin)/2.3548200
fwhmlabel.AddText("FWHM/2.35 = %1.2f GeV" % reducedFWHM)
helper.objs.append(fwhmlabel)
hplotcompint.SetTitle("");
hplotcompint.GetXaxis().SetNoExponent(True);
hplotcompint.GetXaxis().SetTitle("m_{#gamma#gamma} (GeV)");
hplotcompint.GetXaxis().SetNdivisions(509);
## hplotcompint.GetYaxis().SetTitle("A.U.");
## hplotcompint.GetYaxis().SetRangeUser(0.,hplotcompint.GetMaximum()*1.4);
hplotcompint.Draw();
leg.Draw("same")
label.Draw("same")
clabel.Draw("same")
fwhmarrow.Draw("<>")
fwhmlabel.Draw("same")
plot_ds.sumEntries()
if options.doWebPage:
hpl = HtmlPlot(canv,False,"",True,True,True)
hpl.caption("<i>%s</i>" % canv.GetTitle())
row.cell( hpl )
else:
if os.path.isdir(options.outdir) is False:
os.mkdir(options.outdir)
for ext in "C","png","pdf":
canv.SaveAs( os.path.join(options.outdir,"%s.%s" % (canv.GetName(), ext)) )
if "comb" in c:
ip = 0
if options.doWebPage:
print "Creating pages..."
hth.dump()
for f in helper.files:
f.Close()
gROOT.Reset()
from pprint import pprint
pprint(helper)
print 'Summary statistics per event class'
print 'Cat\tSignal\t\tData/GeV (in %3.1f+/-10)\tsigEff\tFWHM/2.35'%options.mH
sigTotal=0.
dataTotal=0.
for c in categories:
sigVal = helper.histos["sumEntries%s"%c]
datVal = helper.histos["data_sumEntries%s"%c]
sigTotal+=sigVal
dataTotal+=datVal
for c in categories:
sigVal = helper.histos["sumEntries%s"%c]
datVal = helper.histos["data_sumEntries%s"%c]
effSig = 0.5*(helper.histos["eff_sigma%s"%c][1]-helper.histos["eff_sigma%s"%c][0])
fwhm = (helper.histos["FWHM%s"%c][1]-helper.histos["FWHM%s"%c][0]) / 2.3548200
print c, '\t%3.1f (%3.1f%%)\t%3.1f (%3.1f%%)\t\t\t%2.2f\t%2.2f'%(sigVal,100.*sigVal/sigTotal,datVal/(10.+10.),100.*datVal/dataTotal,effSig,fwhm)
print "Done."
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(
description=
'Script that creates combine datacards and corresponding RooFit workspaces',
epilog=usage)
parser.add_argument("--inputData",
dest="inputData",
required=True,
help="Input data spectrum",
metavar="INPUT_DATA")
parser.add_argument("--dataHistname",
dest="dataHistname",
required=True,
help="Data histogram name",
metavar="DATA_HISTNAME")
parser.add_argument("--inputSig",
dest="inputSig",
required=True,
help="Input signal shapes",
metavar="INPUT_SIGNAL")
parser.add_argument("-f",
"--final_state",
dest="final_state",
required=True,
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("-f2",
"--type",
dest="atype",
required=True,
help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument(
"-o",
"--output_path",
dest="output_path",
required=True,
help="Output path where datacards and workspaces will be stored",
metavar="OUTPUT_PATH")
parser.add_argument(
"-l",
"--lumi",
dest="lumi",
required=True,
default=1000.,
type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument(
"--massMin",
dest="massMin",
default=500,
type=int,
help=
"Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument(
"--massMax",
dest="massMax",
default=1200,
type=int,
help=
"Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument(
"--p1",
dest="p1",
default=5.0000e-03,
type=float,
help="Fit function p1 parameter (default: %(default)e)",
metavar="P1")
parser.add_argument(
"--p2",
dest="p2",
default=9.1000e+00,
type=float,
help="Fit function p2 parameter (default: %(default)e)",
metavar="P2")
parser.add_argument(
"--p3",
dest="p3",
default=5.0000e-01,
type=float,
help="Fit function p3 parameter (default: %(default)e)",
metavar="P3")
parser.add_argument(
"--lumiUnc",
dest="lumiUnc",
required=True,
type=float,
help="Relative uncertainty in the integrated luminosity",
metavar="LUMI_UNC")
parser.add_argument("--jesUnc",
dest="jesUnc",
type=float,
help="Relative uncertainty in the jet energy scale",
metavar="JES_UNC")
parser.add_argument(
"--jerUnc",
dest="jerUnc",
type=float,
help="Relative uncertainty in the jet energy resolution",
metavar="JER_UNC")
parser.add_argument(
"--sqrtS",
dest="sqrtS",
default=13000.,
type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3",
dest="fixP3",
default=False,
action="store_true",
help="Fix the fit function p3 parameter")
parser.add_argument("--runFit",
dest="runFit",
default=False,
action="store_true",
help="Run the fit")
parser.add_argument("--fitBonly",
dest="fitBonly",
default=False,
action="store_true",
help="Run B-only fit")
parser.add_argument("--fixBkg",
dest="fixBkg",
default=False,
action="store_true",
help="Fix all background parameters")
parser.add_argument("--decoBkg",
dest="decoBkg",
default=False,
action="store_true",
help="Decorrelate background parameters")
parser.add_argument("--fitStrategy",
dest="fitStrategy",
type=int,
default=1,
help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug",
dest="debug",
default=False,
action="store_true",
help="Debug printout")
parser.add_argument(
"--postfix",
dest="postfix",
default='',
help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes",
dest="pyes",
default=False,
action="store_true",
help="Make files for plots")
parser.add_argument("--jyes",
dest="jyes",
default=False,
action="store_true",
help="Make files for JES/JER plots")
parser.add_argument(
"--pdir",
dest="pdir",
default='testarea',
help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2",
dest="chi2",
default=False,
action="store_true",
help="Compute chi squared")
parser.add_argument("--widefit",
dest="widefit",
default=False,
action="store_true",
help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument(
"--mass",
type=int,
nargs='*',
default=1000,
help=
"Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument(
"--massrange",
type=int,
nargs=3,
help="Define a range of masses to be produced. Format: min max step",
metavar=('MIN', 'MAX', 'STEP'))
mass_group.add_argument("--masslist",
help="List containing mass information")
args = parser.parse_args()
if args.atype == 'hG':
fstr = "bbhGGBB"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'hR':
fstr = "bbhRS"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'lG':
fstr = "bblGGBB"
in2 = 'bcorrbin/binmodl.root'
else:
fstr = "bblRS"
in2 = 'bcorrbin/binmodl.root'
# check if the output directory exists
if not os.path.isdir(os.path.join(os.getcwd(), args.output_path)):
os.mkdir(os.path.join(os.getcwd(), args.output_path))
# mass points for which resonance shapes will be produced
masses = []
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX + STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py", ""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
inputData = TFile(args.inputData)
# input data histogram
hData = inputData.Get(args.dataHistname)
inData2 = TFile(in2)
hData2 = inData2.Get('h_data')
# input sig file
inputSig = TFile(args.inputSig)
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.massMin), float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.final_state, int(mass))
# get signal shape
hSig = inputSig.Get("h_" + args.final_state + "_" + str(int(mass)))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(mjj),
hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries() / hSig.Integral())
signal = RooHistPdf('signal', 'signal', RooArgSet(mjj), rooSigHist)
signal_norm = RooRealVar('signal_norm', 'signal_norm', 0, -1e+05,
1e+05)
signal_norm2 = RooRealVar('signal_norm2', 'signal_norm2', 0, -1e+05,
1e+05)
signal_norm3 = RooRealVar('signal_norm3', 'signal_norm3', 0, -1e+05,
1e+05)
signal_norm4 = RooRealVar('signal_norm4', 'signal_norm4', 0, -1e+05,
1e+05)
signal_norm5 = RooRealVar('signal_norm5', 'signal_norm5', 0, -1e+05,
1e+05)
if args.fitBonly:
signal_norm.setConstant()
signal_norm2.setConstant()
signal_norm3.setConstant()
signal_norm4.setConstant()
signal_norm5.setConstant()
p1 = RooRealVar('p1', 'p1', args.p1, 0., 100.)
p2 = RooRealVar('p2', 'p2', args.p2, 0., 60.)
p3 = RooRealVar('p3', 'p3', args.p3, -10., 10.)
p4 = RooRealVar('p4', 'p4', 5.6, -50., 50.)
p5 = RooRealVar('p5', 'p5', 10., -50., 50.)
p6 = RooRealVar('p6', 'p6', .016, -50., 50.)
p7 = RooRealVar('p7', 'p7', 8., -50., 50.)
p8 = RooRealVar('p8', 'p8', .22, -50., 50.)
p9 = RooRealVar('p9', 'p9', 14.1, -50., 50.)
p10 = RooRealVar('p10', 'p10', 8., -50., 50.)
p11 = RooRealVar('p11', 'p11', 4.8, -50., 50.)
p12 = RooRealVar('p12', 'p12', 7., -50., 50.)
p13 = RooRealVar('p13', 'p13', 7., -50., 50.)
p14 = RooRealVar('p14', 'p14', 7., -50., 50.)
p15 = RooRealVar('p15', 'p15', 1., -50., 50.)
p16 = RooRealVar('p16', 'p16', 9., -50., 50.)
p17 = RooRealVar('p17', 'p17', 0.6, -50., 50.)
if args.fixP3: p3.setConstant()
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p1, p2, p3))
dataInt = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm = RooRealVar('background_norm', 'background_norm',
dataInt, 0., 1e+08)
background2 = RooGenericPdf(
'background2',
'(pow(@0/%.1f,-@1)*pow(1-@0/%.1f,@2))' % (sqrtS, sqrtS),
RooArgList(mjj, p4, p5))
dataInt2 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background2_norm = RooRealVar('background2_norm', 'background2_norm',
dataInt2, 0., 1e+08)
background3 = RooGenericPdf('background3',
'(1/pow(@1+@0/%.1f,@2))' % (sqrtS),
RooArgList(mjj, p6, p7))
dataInt3 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background3_norm = RooRealVar('background3_norm', 'background3_norm',
dataInt3, 0., 1e+08)
background4 = RooGenericPdf(
'background4',
'(1/pow(@1+@2*@0/%.1f+pow(@0/%.1f,2),@3))' % (sqrtS, sqrtS),
RooArgList(mjj, p8, p9, p10))
dataInt4 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background4_norm = RooRealVar('background4_norm', 'background4_norm',
dataInt4, 0., 1e+08)
background5 = RooGenericPdf(
'background5',
'(pow(@0/%.1f,-@1)*pow(1-pow(@0/%.1f,1/3),@2))' % (sqrtS, sqrtS),
RooArgList(mjj, p11, p12))
dataInt5 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background5_norm = RooRealVar('background5_norm', 'background5_norm',
dataInt5, 0., 1e+08)
background6 = RooGenericPdf(
'background6', '(pow(@0/%.1f,2)+@1*@0/%.1f+@2)' % (sqrtS, sqrtS),
RooArgList(mjj, p13, p14))
dataInt6 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm6 = RooRealVar('background_norm6', 'background_norm6',
dataInt6, 0., 1e+08)
background7 = RooGenericPdf(
'background7',
'((-1+@1*@0/%.1f)*pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p15, p16, p17))
dataInt7 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm7 = RooRealVar('background_norm7', 'background_norm7',
dataInt7, 0., 1e+08)
#Extend PDFs
exts = RooExtendPdf('extsignal', 'Extended Signal Pdf', signal,
signal_norm)
extb = RooExtendPdf('extbackground', 'Extended Background Pdf',
background, background_norm)
exts2 = RooExtendPdf('extsignal2', 'Extended Signal Pdf2', signal,
signal_norm2)
extb2 = RooExtendPdf('extbackground2', 'Extended Background Pdf2',
background2, background2_norm)
exts3 = RooExtendPdf('extsignal3', 'Extended Signal Pdf3', signal,
signal_norm3)
extb3 = RooExtendPdf('extbackground3', 'Extended Background Pdf3',
background3, background3_norm)
exts4 = RooExtendPdf('extsignal4', 'Extended Signal Pdf4', signal,
signal_norm4)
extb4 = RooExtendPdf('extbackground4', 'Extended Background Pdf4',
background4, background4_norm)
exts5 = RooExtendPdf('extsignal5', 'Extended Signal Pdf5', signal,
signal_norm5)
extb5 = RooExtendPdf('extbackground5', 'Extended Background Pdf5',
background5, background5_norm)
# S+B model
model = RooAddPdf("model", "s+b", RooArgList(extb, exts))
model2 = RooAddPdf("model2", "s+b2", RooArgList(extb2, exts2))
model3 = RooAddPdf("model3", "s+b3", RooArgList(extb3, exts3))
model4 = RooAddPdf("model4", "s+b4", RooArgList(extb4, exts4))
model5 = RooAddPdf("model5", "s+b5", RooArgList(extb5, exts5))
#model6 = RooAddPdf("model6","s+b6",RooArgList(background6,signal),RooArgList(background_norm6,signal_norm))
#model7 = RooAddPdf("model7","s+b7",RooArgList(background7,signal),RooArgList(background_norm7,signal_norm))
rooDataHist = RooDataHist('rooDatahist', 'rooDathist', RooArgList(mjj),
hData)
if args.runFit:
mframe = mjj.frame()
rooDataHist.plotOn(mframe, ROOT.RooFit.Name("setonedata"))
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
model.plotOn(mframe, ROOT.RooFit.Name("model1"),
ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res2 = model2.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model2.plotOn(mframe, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res3 = model3.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model3.plotOn(mframe, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res4 = model4.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model4.plotOn(mframe, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res5 = model5.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model5.plotOn(mframe, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
# res6 = model6.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model6.plotOn(mframe, ROOT.RooFit.Name("model6"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kPink))
# res7 = model7.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model7.plotOn(mframe, ROOT.RooFit.Name("model7"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kAzure))
rooDataHist2 = RooDataHist('rooDatahist2', 'rooDathist2',
RooArgList(mjj), hData2)
# rooDataHist2.plotOn(mframe, ROOT.RooFit.Name("data"))
if args.pyes:
c = TCanvas("c", "c", 800, 800)
mframe.SetAxisRange(300., 1300.)
c.SetLogy()
# mframe.SetMaximum(10)
# mframe.SetMinimum(1)
mframe.Draw()
fitname = args.pdir + '/5funcfit_m' + str(mass) + fstr + '.pdf'
c.SaveAs(fitname)
# cpull = TCanvas("cpull","cpull",800,800)
# pulls = mframe.pullHist("data","model1")
# pulls.Draw("ABX")
# pullname = args.pdir+'/pull_m'+str(mass)+fstr+'.pdf'
# cpull.SaveAs(pullname)
# cpull2 = TCanvas("cpull2","cpull2",800,800)
# pulls2 = mframe.pullHist("setonedata","model1")
# pulls2.Draw("ABX")
# pull2name = args.pdir+'/pull2_m'+str(mass)+fstr+'.pdf'
# cpull2.SaveAs(pull2name)
if args.widefit:
mframew = mjj.frame()
rooDataHist2.plotOn(mframew, ROOT.RooFit.Name("data"))
res6 = model.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model.plotOn(mframew, ROOT.RooFit.Name("model1"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res7 = model2.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model2.plotOn(mframew, ROOT.RooFit.Name("model2"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res8 = model3.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model3.plotOn(mframew, ROOT.RooFit.Name("model3"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res9 = model4.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model4.plotOn(mframew, ROOT.RooFit.Name("model4"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res10 = model5.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model5.plotOn(mframew, ROOT.RooFit.Name("model5"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
if args.pyes:
c = TCanvas("c", "c", 800, 800)
mframew.SetAxisRange(300., 1300.)
c.SetLogy()
# mframew.SetMaximum(10)
# mframew.SetMinimum(1)
mframew.Draw()
fitname = args.pdir + '/5funcfittowide_m' + str(
mass) + fstr + '.pdf'
c.SaveAs(fitname)
cpull = TCanvas("cpull", "cpull", 800, 800)
pulls = mframew.pullHist("data", "model1")
pulls.Draw("ABX")
pullname = args.pdir + '/pullwidefit_m' + str(
mass) + fstr + '.pdf'
cpull.SaveAs(pullname)
if args.chi2:
fullInt = model.createIntegral(RooArgSet(mjj))
norm = dataInt / fullInt.getVal()
chi1 = 0.
fullInt2 = model2.createIntegral(RooArgSet(mjj))
norm2 = dataInt2 / fullInt2.getVal()
chi2 = 0.
fullInt3 = model3.createIntegral(RooArgSet(mjj))
norm3 = dataInt3 / fullInt3.getVal()
chi3 = 0.
fullInt4 = model4.createIntegral(RooArgSet(mjj))
norm4 = dataInt4 / fullInt4.getVal()
chi4 = 0.
fullInt5 = model5.createIntegral(RooArgSet(mjj))
norm5 = dataInt5 / fullInt5.getVal()
chi5 = 0.
for i in range(args.massMin, args.massMax):
new = 0
new2 = 0
new3 = 0
new4 = 0
new5 = 0
height = hData.GetBinContent(i)
xLow = hData.GetXaxis().GetBinLowEdge(i)
xUp = hData.GetXaxis().GetBinLowEdge(i + 1)
obs = height * (xUp - xLow)
mjj.setRange("intrange", xLow, xUp)
integ = model.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp = integ.getVal() * norm
new = pow(exp - obs, 2) / exp
chi1 = chi1 + new
integ2 = model2.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp2 = integ2.getVal() * norm2
new2 = pow(exp2 - obs, 2) / exp2
chi2 = chi2 + new2
integ3 = model3.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp3 = integ3.getVal() * norm3
new3 = pow(exp3 - obs, 2) / exp3
chi3 = chi3 + new3
integ4 = model4.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp4 = integ4.getVal() * norm4
if exp4 != 0:
new4 = pow(exp4 - obs, 2) / exp4
else:
new4 = 0
chi4 = chi4 + new4
integ5 = model5.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp5 = integ5.getVal() * norm5
new5 = pow(exp5 - obs, 2) / exp5
chi5 = chi5 + new5
print "chi1 %d " % (chi1)
print "chi2 %d " % (chi2)
print "chi3 %d " % (chi3)
print "chi4 %d " % (chi4)
print "chi5 %d " % (chi5)
if not args.decoBkg:
print " "
res.Print()
res2.Print()
res3.Print()
res4.Print()
res5.Print()
# res6.Print()
# res7.Print()
# decorrelated background parameters for Bayesian limits
if args.decoBkg:
signal_norm.setConstant()
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
res.Print()
## temp workspace for the PDF diagonalizer
w_tmp = RooWorkspace("w_tmp")
deco = PdfDiagonalizer("deco", w_tmp, res)
# here diagonalizing only the shape parameters since the overall normalization is already decorrelated
background_deco = deco.diagonalize(background)
print "##################### workspace for decorrelation"
w_tmp.Print("v")
print "##################### original parameters"
background.getParameters(rooDataHist).Print("v")
print "##################### decorrelated parameters"
# needed if want to evaluate limits without background systematics
if args.fixBkg:
w_tmp.var("deco_eig1").setConstant()
w_tmp.var("deco_eig2").setConstant()
if not args.fixP3: w_tmp.var("deco_eig3").setConstant()
background_deco.getParameters(rooDataHist).Print("v")
print "##################### original pdf"
background.Print()
print "##################### decorrelated pdf"
background_deco.Print()
# release signal normalization
signal_norm.setConstant(kFALSE)
# set the background normalization range to +/- 5 sigma
bkg_val = background_norm.getVal()
bkg_error = background_norm.getError()
background_norm.setMin(bkg_val - 5 * bkg_error)
background_norm.setMax(bkg_val + 5 * bkg_error)
background_norm.Print()
# change background PDF names
background.SetName("background_old")
background_deco.SetName("background")
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norm.setConstant()
p1.setConstant()
p2.setConstant()
p3.setConstant()
# -----------------------------------------
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX() + 1)
# JES and JER uncertainties
if args.jesUnc != None or args.jerUnc != None:
sigCDF.SetPoint(0, 0., 0.)
integral = 0.
for i in range(1, hSig.GetNbinsX() + 1):
x = hSig.GetXaxis().GetBinLowEdge(i + 1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i, x, integral)
if args.jesUnc != None:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if args.jerUnc != None:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jes = 1. - args.jesUnc
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + args.jesUnc
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp',
'hSig_JESUp',
RooArgList(mjj),
hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist(
'hSig_JESDown', 'hSig_JESDown', RooArgList(mjj),
hSig_Syst['JESDown'])
if args.jyes:
c2 = TCanvas("c2", "c2", 800, 800)
mframe2 = mjj.frame(ROOT.RooFit.Title("JES One Sigma Shifts"))
mframe2.SetAxisRange(args.massMin, args.massMax)
hSig_Syst_DataHist['JESUp'].plotOn(
mframe2, ROOT.RooFit.Name("JESUP"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kRed),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JESDown'].plotOn(
mframe2, ROOT.RooFit.Name("JESDOWN"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe2, ROOT.RooFit.DataError(2),
ROOT.RooFit.Name("SIG"),
ROOT.RooFit.DrawOption("L"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe2.Draw()
mframe2.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7, 0.8, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(mframe2.findObject("SIG"), "Signal Model", "l")
leg.AddEntry(mframe2.findObject("JESUP"), "+1 Sigma", "l")
leg.AddEntry(mframe2.findObject("JESDOWN"), "-1 Sigma", "l")
leg.Draw()
jesname = args.pdir + '/jes_m' + str(mass) + fstr + '.pdf'
c2.SaveAs(jesname)
# produce JER signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jer = 1. - args.jerUnc
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + args.jerUnc
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp',
'hSig_JERUp',
RooArgList(mjj),
hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist(
'hSig_JERDown', 'hSig_JERDown', RooArgList(mjj),
hSig_Syst['JERDown'])
if args.jyes:
c3 = TCanvas("c3", "c3", 800, 800)
mframe3 = mjj.frame(ROOT.RooFit.Title("JER One Sigma Shifts"))
mframe3.SetAxisRange(args.massMin, args.massMax)
hSig_Syst_DataHist['JERUp'].plotOn(
mframe3, ROOT.RooFit.Name("JERUP"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kRed),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JERDown'].plotOn(
mframe3, ROOT.RooFit.Name("JERDOWN"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe3, ROOT.RooFit.DrawOption("L"),
ROOT.RooFit.Name("SIG"),
ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe3.Draw()
mframe3.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7, 0.8, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(mframe3.findObject("SIG"), "Signal Model", "l")
leg.AddEntry(mframe3.findObject("JERUP"), "+1 Sigma", "l")
leg.AddEntry(mframe3.findObject("JERDOWN"), "-1 Sigma", "l")
leg.Draw()
jername = args.pdir + '/jer_m' + str(mass) + fstr + '.pdf'
c3.SaveAs(jername)
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
dcName = 'datacard_' + args.final_state + '_m' + str(
mass) + postfix + '.txt'
wsName = 'workspace_' + args.final_state + '_m' + str(
mass) + postfix + '.root'
w = RooWorkspace('w', 'workspace')
getattr(w, 'import')(rooSigHist, RooFit.Rename("signal"))
if args.jesUnc != None:
getattr(w, 'import')(hSig_Syst_DataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if args.jerUnc != None:
getattr(w, 'import')(hSig_Syst_DataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w, 'import')(background_deco, ROOT.RooCmdArg())
else:
getattr(w, 'import')(background, ROOT.RooCmdArg(),
RooFit.Rename("background"))
getattr(w, 'import')(background2, ROOT.RooCmdArg(),
RooFit.Rename("background2"))
getattr(w, 'import')(background3, ROOT.RooCmdArg(),
RooFit.Rename("background3"))
getattr(w, 'import')(background4, ROOT.RooCmdArg(),
RooFit.Rename("background4"))
getattr(w, 'import')(background5, ROOT.RooCmdArg(),
RooFit.Rename("background5"))
getattr(w, 'import')(background_norm, ROOT.RooCmdArg(),
RooFit.Rename("background_norm"))
getattr(w, 'import')(background2_norm, ROOT.RooCmdArg(),
RooFit.Rename("background2_norm"))
getattr(w, 'import')(background3_norm, ROOT.RooCmdArg(),
RooFit.Rename("background3_norm"))
getattr(w, 'import')(background4_norm, ROOT.RooCmdArg(),
RooFit.Rename("background4_norm"))
getattr(w, 'import')(background5_norm, ROOT.RooCmdArg(),
RooFit.Rename("background5_norm"))
getattr(w, 'import')(res)
getattr(w, 'import')(res2)
getattr(w, 'import')(res3)
getattr(w, 'import')(res4)
getattr(w, 'import')(res5)
getattr(w, 'import')(background_norm, ROOT.RooCmdArg())
getattr(w, 'import')(signal_norm, ROOT.RooCmdArg())
getattr(w, 'import')(rooDataHist, RooFit.Rename("data_obs"))
w.Print()
w.writeToFile(os.path.join(args.output_path, wsName))
beffUnc = 0.3
boffUnc = 0.06
datacard = open(os.path.join(args.output_path, dcName), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc != None or args.jerUnc != None:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * ' + wsName + ' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n' % (1. + args.lumiUnc))
datacard.write('beff lnN %f -\n' % (1. + beffUnc))
datacard.write('boff lnN %f -\n' % (1. + boffUnc))
datacard.write('bkg lnN - 1.03\n')
if args.jesUnc != None:
datacard.write('JES shape 1 -\n')
if args.jerUnc != None:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
if not args.fixP3: datacard.write('deco_eig3 flatParam\n')
else:
datacard.write('p1 flatParam\n')
datacard.write('p2 flatParam\n')
if not args.fixP3: datacard.write('p3 flatParam\n')
datacard.close()
print '>> Datacards and workspaces created and stored in %s/' % (
os.path.join(os.getcwd(), args.output_path))
def main():
global beamEnergy
# Parse all command line arguments using the argparse module.
parser = argparse.ArgumentParser(
description='PyRoot analysis demostrating the us of a DST.')
parser.add_argument("dst_file", help="ROOT DST file to process")
parser.add_argument("-o", "--output", help="Name of output pdf file")
parser.add_argument("-m", "--mc", help="is MonteCarlo")
parser.add_argument("-p", "--pulser", help="is Pulser")
parser.add_argument("-e", "--energy", help="beam energy")
args = parser.parse_args()
# If an output file name was not specified, set a default name and warn
# the user
if args.output:
output_file = args.output
else:
output_file = "analysis_output.root"
print "[ HPS ANALYSIS ]: An output file name was not specified. Setting the name to "
print output_file
print "[ HPS ANALYSIS ]: Output file is " + output_file
isMC = False
if args.mc:
print "[ HPS ANALYSIS ]: Setting to run as MC"
isMC = True
isPulser = False
if args.pulser:
print "[ HPS ANALYSIS ]: Setting to run from a pulser file"
isPulser = True
if args.energy:
print 'Setting beam energy to ' + args.energy
beamEnergy = float(args.energy)
myhist.setEnergyScales(beamEnergy)
#################################
# Event Selection
################################
#clean up event first
#### nominal selection
nTrkMax = 10
nTrkMin = 2
nPosMax = 3
###### two tracks (e+/e-) exactly
# nTrkMax=2
# nTrkMin=2
# nPosMax=1
###### more than 1 electron
# nTrkMax=10
# nTrkMin=3
# nPosMax=1
###################
#v0 cuts
v0Chi2 = 10
#ESum -- full region
v0PzMax = 1.2 * beamEnergy
v0PzMin = 0.55 * beamEnergy
#ESum -- Radiative region
# v0PzMax=1.2
# v0PzMin=0.80
v0PyMax = 0.2 #absolute value
v0PxMax = 0.2 #absolute value
v0VzMax = 25.0 # mm from target
v0VyMax = 1.0 # mm from target
v0VxMax = 2.0 # mm from target
# track quality cuts
trkChi2 = 10
beamCut = 0.8 * beamEnergy
minPCut = 0.05
trkPyMax = 0.2
trkPxMax = 0.2
# slopeCut=0.03
slopeCut = 0.0
trkDeltaT = 4 #ns
cluDeltaT = 2 #ns
cluTrkDeltaT = 4 #ns
##############
# ESum slices; upper limits
nSlicesESum = 5
esumMin = 0.55
esumMax = 1.2
sliceSizeESum = 0.1 #100MeV starting at esumMin
##############
trackKiller = False
tkThreshold = 0.5 #GeV, below this start killing tracks
tkThreshEff = 1.0
tkLowPoint = 0.20
tkLowPointEff = 0.40
# tkSlope=2.6
# tkIntercept=-0.04
#calculate tkSlope and Intercept
tkSlope = (tkThreshEff - tkLowPointEff) / (tkThreshold - tkLowPoint)
tkIntercept = tkThreshEff - tkSlope * tkThreshold
##############
requireECalMatch = True
requireECalFiducial = False
requireECalSuperFiducial = False
useGBL = True
# Open the ROOT file
# root_file = ROOT.TFile(str(args.dst_file))
# Get the TTree "HPS_EVENT" containing the HpsEvent branch and all
# other colletions
# tree = root_file.Get("HPS_Event")
#use a TChain
print "[ HPS ANALYSIS ]: Reading in root chain from " + args.dst_file
tree = ROOT.TChain("HPS_Event")
tree.Add(str(args.dst_file) + "*")
# Create an HpsEvent object in order to read the TClonesArray
# collections
hps_event = HpsEvent()
b_hps_event = tree.SetBranchAddress("Event", ROOT.AddressOf(hps_event))
#--- Analysis ---#
#----------------#
#counters
nEvents = 0
nPassBasicCuts = 0
nPassV0Cuts = 0
nPassTrkCuts = 0
nPassNCand = 0
nPassECalMatch = 0
nFakeTri = 0
if 1 == 0:
extraElectronProb = 0.0
dataNele = 170.0
btNele = 1.4
hf = ROOT.TFile(
"OutputHistograms/Data/hps_005772_pass6_useGBL_ECalMatch_SuperFiducialCut.root"
)
hfMC = ROOT.TFile(
"OutputHistograms/MC/beam-tri_HPS-EngRun2015-Nominal-v4-4_pass6_useGBL_ECalMatch_SuperFiducialCut.root"
)
pele = ROOT.RooRealVar("pele", "pele", 0, 1)
peleHist = hf.Get("raweleMom")
peleMCHist = hfMC.Get("raweleMom")
peleHist.Scale(1 / dataNele)
peleMCHist.Scale(1 / btNele)
peleHist.Add(
peleMCHist, -1.0
) #subtract the MC from the data to get the distribution of extra tracks...this is cheating!
for i in xrange(0, peleHist.GetNbinsX()):
print "Electron Momentum bin i = " + str(peleHist.GetBinContent(i))
if peleHist.GetBinContent(i) < 0:
peleHist.SetBinContent(i, 0)
peleHist.Print("V")
peleDH = RooDataHist("peleDH", "peleDH", ROOT.RooArgList(pele),
peleHist)
peleDH.Print("V")
extraElectronPdf = RooHistPdf("extraElectronPdf", "extraElectronPdf",
ROOT.RooArgSet(pele), peleDH)
print 'pdf is made...printing info'
extraElectronPdf.Print("V")
# if isMC and random.random()<extraElectronProb :
#add an extra electron based on electron momentum
print 'generating events'
newElePData = extraElectronPdf.generate(ROOT.RooArgSet(pele),
1000000.0, True, False)
newElePData.Print("V")
seedCnt = 0
# Loop over all events in the file
for entry in xrange(0, tree.GetEntries()):
# Print the event number every 500 events
if (entry + 1) % 10000 == 0: print "Event " + str(entry + 1)
tree.GetEntry(entry)
if not hps_event.isPair1Trigger() and not isMC and not isPulser:
continue
nEvents += 1
addFakeEle = False
if 1 == 0:
if isMC and random.random() < extraElectronProb:
#add an extra electron based on electron momentum
addFakeEle = True
newEleP = newElePData.get(seedCnt).find("pele").getVal()
seedCnt = seedCnt + 1
# print 'Inserting an electron with momentum = '+str(newEleP)
# newEleCluster=hps_event.addEcalCluster();
# newEleTrack=hps_event.addTrack();
# print 'numbe of HpsParticles before = '+str(hps_event.getNumberOfParticles(HpsParticle.FINAL_STATE_PARTICLE))
# newEle=hps_event.addParticle(HpsParticle.FINAL_STATE_PARTICLE)
# print 'numbe of HpsParticles after = '+str(hps_event.getNumberOfParticles(HpsParticle.FINAL_STATE_PARTICLE))
newEle = HpsParticle()
newEle.setCharge(-1)
newEle.setPDG(11)
newEle.setEnergy(newEleP)
sign = 1
if random.random() < 0.5:
sign = -1
newEleMom = [
math.sin(0.03) * newEleP, sign * math.sin(0.04) * newEleP,
math.cos(0.04) * newEleP
]
newEle.setMomentum(np.array(newEleMom))
#fake track info
newEleTrack = SvtTrack()
newEleTrack.setTrackParameters(0.0, 0.03, 0.0001, sign * 0.04,
0.0)
newEleTrack.setTrackTime(40.0)
newEleTrack.setParticle(newEle)
newEleTrack.setChi2(3.0)
newEleTrack.setPositionAtEcal(
np.array([300.0, sign * 50.0, 1350.0]))
#fake cluster info
newEleCluster = EcalCluster()
newEleCluster.setEnergy(newEleP)
foobar = [300.0, sign * 50.0, 1350.0]
newEleCluster.setPosition(np.array(foobar, dtype='float32'))
newEle.addTrack(newEleTrack)
newEle.addCluster(newEleCluster)
# print 'fake electron cluster x ' + str(newEle.getClusters().First().getPosition()[0])
# Loop over all tracks in the event
npositrons = 0
n_tracks = 0
for track_n in xrange(0, hps_event.getNumberOfTracks()):
track = hps_event.getTrack(track_n)
if track is None:
continue
# if useGBL and track.getParticle().getType()<32 : continue
# if not useGBL and track.getParticle().getType()>31 : continue
if trkMatchAndFiducial(
track.getParticle(),
requireECalSuperFiducial) and trkMomentum(
track, minPCut, beamCut
): # count only matched tracks in defined fiducial region
n_tracks += 1
if track.getCharge() > 0:
npositrons += 1
myhist.rawposMom.Fill(pMag(track.getMomentum()))
else:
myhist.raweleMom.Fill(pMag(track.getMomentum()))
# findWABPair(track.getParticle(),hps_event)
if addFakeEle:
myhist.raweleMom.Fill(newEle.getEnergy())
n_tracks = n_tracks + 1
# print "nTracks = "+str(n_tracks)+"; nPositrons = "+str(npositrons)
# if n_tracks/2.0>nTrkMax : continue #do this very dumb thing (divide by 2 to un-double count GBL tracks)
# if n_tracks/2.0<2: continue
myhist.nTrk.Fill(n_tracks)
myhist.nPos.Fill(npositrons)
myhist.nEle.Fill(n_tracks - npositrons)
myhist.nClust.Fill(hps_event.getNumberOfEcalClusters())
if n_tracks > nTrkMax: continue
if n_tracks < nTrkMin: continue
if npositrons < 1 or npositrons > nPosMax: continue
nPassBasicCuts += 1
# print "passed basic cuts"
candidateList = []
bestCandidate = -99
nCandidate = 0
# loop over all v0 candidates...
for uc_index in xrange(
0,
hps_event.getNumberOfParticles(HpsParticle.UC_V0_CANDIDATE)):
particle = hps_event.getParticle(HpsParticle.UC_V0_CANDIDATE,
uc_index)
if useGBL and particle.getType() < 32: continue
if not useGBL and particle.getType() > 31: continue
# print "found one..."
vchi2 = particle.getVertexFitChi2()
vposition = particle.getVertexPosition()
vmomentum = particle.getMomentum()
if vchi2 > v0Chi2: continue
# use the measured sum of momentum
# if vmomentum[2]>v0PzMax : continue
# if vmomentum[2]<v0PzMin : continue
#recon'ed vertex position cuts
if abs(vposition[0]) > v0VxMax: continue
if abs(vposition[1]) > v0VyMax: continue
# if abs(vposition[2])>v0VzMax :continue
# Only look at particles that have two daugther particles...
daughter_particles = particle.getParticles()
if daughter_particles.GetSize() != 2: continue
# Only look at particles that are composed of e+e- pairs
if daughter_particles.At(0).getCharge() * daughter_particles.At(
1).getCharge() > 0:
continue
# print "Passed daughter number cuts"
electron = daughter_particles.At(0)
positron = daughter_particles.At(1)
if daughter_particles.At(0).getCharge() > 0:
electron = daughter_particles.At(1)
positron = daughter_particles.At(0)
pEle = electron.getMomentum()
pPos = positron.getMomentum()
v0Sum = pMag(pSum(pEle, pPos))
#total momentum sum cuts
if v0Sum > v0PzMax: continue
if v0Sum < v0PzMin: continue
nPassV0Cuts += 1
# print "Passed v0 cuts"
############# tracking cuts
#momentum cuts...get rid of very soft or very hard tracks
if pMag(pEle) > beamCut or pMag(pPos) > beamCut: continue
if pMag(pEle) < minPCut or pMag(pPos) < minPCut: continue
#top+bottom requirement
if pEle[1] * pPos[1] > 0: continue
# print 'looking at tracks now'
# print len(electron.getTracks())
if len(electron.getTracks()) == 0 or len(
positron.getTracks()) == 0:
continue
eleTrk = electron.getTracks().At(0)
posTrk = positron.getTracks().At(0)
if eleTrk is None or posTrk is None: continue
# eleTrk.Print("v")
#track timing
if eleTrk.getTrackTime() - posTrk.getTrackTime() > trkDeltaT:
continue
#track slope (if any cut)
if abs(eleTrk.getTanLambda()) < slopeCut or abs(
posTrk.getTanLambda()) < slopeCut:
continue
# print 'satisfied timing cuts...'
##############
# track killer part
if isMC and trackKiller:
if pMag(pEle) < tkThreshold: #electron
tkEff = tkSlope * pMag(pEle) + tkIntercept
if random.random() > tkEff:
continue
elif random.random(
) > tkThreshEff: #allow for a flat killer above threshold
continue
if pMag(pPos) < tkThreshold: # positron
tkEff = tkSlope * pMag(pPos) + tkIntercept
if random.random() > tkEff:
continue
elif random.random(
) > tkThreshEff: #allow for a flat killer above threshold
continue
# end of track killer
##############
nPassTrkCuts += 1
##############
# ECAL matching and timing cuts...also fiducial region cuts...
if requireECalMatch:
if positron.getClusters().GetEntries() == 0:
continue
if electron.getClusters().GetEntries() == 0:
continue
posCluster = positron.getClusters().First()
eleCluster = electron.getClusters().First()
if eleCluster.getClusterTime() - posCluster.getClusterTime(
) > cluDeltaT:
continue
if eleTrk.getTrackTime() - eleCluster.getClusterTime(
) + 43.5 > cluTrkDeltaT:
continue
if posTrk.getTrackTime() - posCluster.getClusterTime(
) + 43.5 > cluTrkDeltaT:
continue
if requireECalFiducial:
#ANTI-fiducial cut
# if myhist.inFiducialRegion(posCluster.getPosition()[0],posCluster.getPosition()[1]) :
# continue
# if myhist.inFiducialRegion(eleCluster.getPosition()[0],eleCluster.getPosition()[1]) :
# continue
#Fiducial cut
if not myhist.inFiducialRegion(
posCluster.getPosition()[0],
posCluster.getPosition()[1]):
continue
if not myhist.inFiducialRegion(
eleCluster.getPosition()[0],
eleCluster.getPosition()[1]):
continue
if requireECalSuperFiducial:
if not myhist.inSuperFiducialRegion(
posCluster.getPosition()[0],
posCluster.getPosition()[1]):
continue
if not myhist.inSuperFiducialRegion(
eleCluster.getPosition()[0],
eleCluster.getPosition()[1]):
continue
nPassECalMatch += 1
##############
#Passed the cuts..append the candidate index
findWABPair(electron, hps_event)
candidateList.append(uc_index)
numCands = len(candidateList)
if addFakeEle:
for track_n in xrange(0, hps_event.getNumberOfTracks()):
track = hps_event.getTrack(track_n)
if track is None:
continue
if trkMatchAndFiducial(track.getParticle()) and trkMomentum(
track, minPCut, beamCut
) and track.getCharge > 0 and newEle.getMomentum(
)[1] * track.getMomentum(
)[1] < 0: # get positron in fudicial region; make sure it's in opposite quadrant
myhist.eSum.Fill(newEle.getEnergy() +
pMag(track.getMomentum()))
numCands += 1
if len(candidateList) == 0:
# print 'made a new trident event'
nFakeTri += 1
myhist.nCand.Fill(numCands)
#########################
# found some candidates...lets fill plots...
#########################
for index in range(0, len(candidateList)):
particle = hps_event.getParticle(HpsParticle.TC_V0_CANDIDATE,
candidateList[index])
myhist.fillCandidateHistograms(particle)
myhist.nTrkCand.Fill(n_tracks)
myhist.nPosCand.Fill(npositrons)
myhist.nEleCand.Fill(n_tracks - npositrons)
myhist.nClustCand.Fill(hps_event.getNumberOfEcalClusters())
# if(nPassTrkCuts>0):
myhist.saveHistograms(output_file)
print "******************************************************************************************"
print "Number of Events:\t\t", nEvents, "\t\t\t", float(
nEvents) / nEvents, "\t\t\t", float(nEvents) / nEvents
print "N(particle) Cuts:\t\t", nPassBasicCuts, "\t\t\t", float(
nPassBasicCuts) / nEvents, "\t\t\t", float(nPassBasicCuts) / nEvents
print "V0 Vertex Cuts:\t\t", nPassV0Cuts, "\t\t\t", float(
nPassV0Cuts) / nPassBasicCuts, "\t\t\t", float(nPassV0Cuts) / nEvents
print "Tracking Cuts:\t\t", nPassTrkCuts, "\t\t\t", float(
nPassTrkCuts) / nPassV0Cuts, "\t\t\t", float(nPassTrkCuts) / nEvents
print "ECal Match Cuts:\t\t", nPassECalMatch, "\t\t\t", float(
nPassECalMatch) / nPassTrkCuts, "\t\t\t", float(
nPassECalMatch) / nEvents
print "Number of Fake Events Added: \t\t", nFakeTri, "\t\t\t", float(
nFakeTri) / nPassECalMatch
def get_fitted_normalisation_from_ROOT(channel, input_files, variable,
met_type, b_tag_bin):
results = {}
initial_values = {}
templates = {}
for variable_bin in variable_bins_ROOT[variable]:
histograms = get_histograms(channel,
input_files,
variable=variable,
met_type=met_type,
variable_bin=variable_bin,
b_tag_bin=b_tag_bin,
rebin=measurement_config.rebin)
# create signal histograms
h_eta_signal = histograms['TTJet'] + histograms['SingleTop']
N_ttbar_before_fit = histograms['TTJet'].Integral()
N_SingleTop_before_fit = histograms['SingleTop'].Integral()
N_vjets_before_fit = histograms['V+Jets'].Integral()
N_qcd_before_fit = histograms['QCD'].Integral()
N_signal_before_fit = N_ttbar_before_fit + N_SingleTop_before_fit
N_ttbar_error_before_fit = sum(histograms['TTJet'].errors())
N_SingleTop_error_before_fit = sum(histograms['SingleTop'].errors())
N_vjets_error_before_fit = sum(histograms['V+Jets'].errors())
N_QCD_error_before_fit = sum(histograms['QCD'].errors())
if (N_SingleTop_before_fit != 0):
TTJet_SingleTop_ratio = N_ttbar_before_fit / N_SingleTop_before_fit
else:
print 'Bin ', variable_bin, ': ttbar/singleTop ratio undefined for %s channel! Setting to 0.' % channel
TTJet_SingleTop_ratio = 0
leptonAbsEta = RooRealVar("leptonAbsEta", "leptonAbsEta", 0., 2.4)
# this has to move to dps.utils.Fitting.py
vars = RooArgList()
vars.add(leptonAbsEta)
vars_set = RooArgSet()
vars_set.add(leptonAbsEta)
n_event_obs = histograms['data'].Integral()
lowerBound = 0.
upperBound = n_event_obs + 10 * sqrt(n_event_obs)
n_init = n_event_obs / 2.
data = RooDataHist("data", "dataset with leptonAbsEta", vars,
histograms['data'])
rh_vj = RooDataHist("rh_vj", "vj", vars, histograms['V+Jets'])
rh_qcd = RooDataHist("rh_qcd", "qcd", vars, histograms['QCD'])
rh_signal = RooDataHist("rh_signal", "signal", vars, h_eta_signal)
pdf_vj = RooHistPdf("pdf_vj", "V+Jets pdf", vars_set, rh_vj, 0)
pdf_qcd = RooHistPdf("pdf_qcd", "QCD pdf ", vars_set, rh_qcd, 0)
pdf_signal = RooHistPdf("pdf_signal", "single top pdf", vars_set,
rh_signal, 0)
# RooRealVar(const char *name, const char *title, Double_t value, Double_t minValue, Double_t maxValue, const char *unit) :
nSignal = RooRealVar("nSignal", "number of single top + ttbar events",
N_signal_before_fit, lowerBound, upperBound,
"event")
nvj = RooRealVar("nvj", "number of V+Jets bgnd events",
N_vjets_before_fit, lowerBound, upperBound, "event")
nqcd = RooRealVar("nqcd", "number of QCD bgnd events",
N_QCD_error_before_fit, lowerBound, upperBound,
"event")
model = RooAddPdf("model", "sig+vj+qcd",
RooArgList(pdf_signal, pdf_vj, pdf_qcd),
RooArgList(nSignal, nvj, nqcd))
vj_constraint = RooGaussian("nvj_constraint", "nvj_constraint", nvj,
RooFit.RooConst(N_vjets_before_fit),
RooFit.RooConst(0.5 * N_vjets_before_fit))
qcd_constraint = RooGaussian("nqcd_constraint", "nqcd_constraint",
nqcd, RooFit.RooConst(N_qcd_before_fit),
RooFit.RooConst(2 * N_qcd_before_fit))
model_with_constraints = RooProdPdf(
"model_with_constraints", "model with gaussian constraints",
RooArgSet(model, vj_constraint, qcd_constraint), RooLinkedList())
model_with_constraints.fitTo(data, RooFit.Minimizer(
"Minuit2",
"Migrad")) #WARNING: number of cores changes the results!!!
# nll = model.createNLL(data, RooFit.NumCPU(2))
# RooMinuit(nll).migrad()
# frame1 = nSignal.frame(RooFit.Bins(100), RooFit.Range(lowerBound, n_event_obs), RooFit.Title("LL and profileLL in nSignal"))
# nll.plotOn(frame1, RooFit.ShiftToZero())
# frame2 = nvj.frame(RooFit.Bins(100), RooFit.Range(lowerBound, n_event_obs), RooFit.Title("LL and profileLL in nvj"))
# nll.plotOn(frame2, RooFit.ShiftToZero())
# frame3 = nqcd.frame(RooFit.Bins(100), RooFit.Range(lowerBound, n_event_obs), RooFit.Title("LL and profileLL in nqcd"))
# nll.plotOn(frame3, RooFit.ShiftToZero())
#
# pll_nSignal = nll.createProfile(nSignal)
# pll_nSignal.plotOn(frame1, RooFit.LineColor(2))
# frame1.SetMinimum(0)
# frame1.SetMaximum(3)
#
# pll_nvj = nll.createProfile(nvj)
# pll_nvj.plotOn(frame2, RooFit.LineColor(2))
# frame2.SetMinimum(0)
# frame2.SetMaximum(3)
#
# pll_nqcd = nll.createProfile(nqcd)
# pll_nqcd.plotOn(frame3, RooFit.LineColor(2))
# frame3.SetMinimum(0)
# frame3.SetMaximum(3)
# c = TCanvas("profilell","profilell",1200, 400)
# c.Divide(3)
# c.cd(1)
# frame1.Draw()
# c.cd(2)
# frame2.Draw()
# c.cd(3)
# frame3.Draw()
# c.SaveAs('profileLL.png')
# model.fitTo(data, RooFit.Minimizer("Minuit2", "Migrad"), RooFit.NumCPU(1))#WARNING: number of cores changes the results!!!
fit_results = {}
fit_results['signal'] = (nSignal.getVal(), nSignal.getError())
fit_results['QCD'] = ufloat(nqcd.getVal(), nqcd.getError())
fit_results['V+Jets'] = ufloat(nvj.getVal(), nvj.getError())
N_ttbar, N_SingleTop = decombine_result(fit_results['signal'],
TTJet_SingleTop_ratio)
fit_results['signal'] = ufloat(nSignal.getVal(), nSignal.getError())
fit_results['TTJet'] = ufloat(N_ttbar)
fit_results['SingleTop'] = ufloat(N_SingleTop)
if results == {}: # empty
for sample in fit_results.keys():
results[sample] = [fit_results[sample]]
else:
for sample in fit_results.keys():
results[sample].append(fit_results[sample])
return results, None, None
mmgMassFunc = w.factory('''FormulaVar::mmgMassFunc(
"((mmgMass - mmgMode) / mmgScale + mmgMode) - fPho*phoScale/100.",
{mmgMass, mmgMode[91.2,70,110], mmgScale[1.,0.1,5], fPho, phoScale}
)''')
mmgMode = w.var('mmgMode')
mmgScale = w.var('mmgScale')
## Get the nominal model for translation, use rho=2
model = RooKeysPdf('model', 'model', mmgMass, mmgData, RooKeysPdf.NoMirror, 2)
## Sample nominal model to binned data, no Poisson fluctuation
modelHist = model.createHistogram('mmgMass', 10000)
modelDH = RooDataHist('modelDH', 'modelDH', RooArgList(mmgMass), modelHist)
## Use the sampled model to build a transformed model, interpolation order=2
tmodel = RooHistPdf('tmodel', 'tmodel', RooArgList(mmgMassFunc),
RooArgList(mmgMass), modelDH, 2)
## Find the mode of the tmodel.
mmgMode.setVal(modelHist.GetBinCenter(modelHist.GetMaximumBin()))
mmgMode.setConstant(True)
fPho.setConstant(True)
## Fit the photon scale
phoScale.setConstant(False)
mmgScale.setConstant(True)
## Make plots
canvases.next('nominal')
mmgFrame = mmgMass.frame(Range(60,120))
mmgData.plotOn(mmgFrame)
DTF_D0sPi_M.setMin(2000)
varset_small = RooArgSet("varset_small")
varset_small.add(LOG_D0_IPCHI2_OWNPV)
varset_small.add(Dst_DTF_D0_CTAU)
varset_small.add(DTF_D0sPi_M)
#Create histogram for time bin 0
dataset_RS_0 = wsp.data("dataset_RS")
dataset_RS_0.SetName("dataset_RS_0")
print "dataset_RS_0 extraction took "+str(datetime.now()-start)+" \n"
start = datetime.now()
hist_RS_0 = RooDataHist("hist_RS_0","hist_RS_0", RooArgSet(LOG_D0_IPCHI2_OWNPV), dataset_RS_0)
print "RooDataHist creation took "+str(datetime.now()-start)+" \n"
start = datetime.now()
shape_RS_0 = RooHistPdf("shape_RS_0", "shape_RS_0", RooArgSet(LOG_D0_IPCHI2_OWNPV), hist_RS_0)
print "RooHistPdf creation took "+str(datetime.now()-start)+" \n"
h_0 = hist_RS_0.createHistogram("LOG_D0_IPCHI2_OWNPV", 100)
#Fage function used to fit signal distribution
#Fix asymmetry paramters from 0 bin, leave mean and width float.
fage_mu = RooRealVar("fage_mu", "fage_mu", 0.5, 0., 1.)
fage_sigma = RooRealVar("fage_sigma", "fage_sigma", 2.0, 0.5, 5.)
fage_epsilon = RooRealVar("fage_epsilon", "fage_epsilon", 0., -1., 1.)
fage_rhol = RooRealVar("fage_rhol", "fage_rhol", 1., 0.1, 10)
fage_rhor = RooRealVar("fage_rhor", "fage_rhor", 1., 0.1, 10)
s_fage = ROOT.fage("s_fage", "s_fage", LOG_D0_IPCHI2_OWNPV, fage_mu, fage_sigma, fage_epsilon, fage_rhol, fage_rhor)
fit_hists_0 = s_fage.fitTo(hist_RS_0,RooFit.SumW2Error(True),RooFit.Save())
fage_epsilon.setConstant(True)
fage_rhol.setConstant(True)
fage_rhor.setConstant(True)
w.var('gm').setVal(-3.)
w.var('gs').setVal(2)
data2 = g.generate(ArgSet(x), 10000)
w.var('gm').setVal(0.)
w.var('gs').setVal(1.)
## 3. Use data 1 to build Keys PDF k1(x|s1,m1)
k = KeysPdf('k', 'k', x, data1, KeysPdf.NoMirror, 2)
## 4. Use k to build HistPDF with _transformed_ x variable t = (x-m)/s
## h1((x-hm)/hs|m1,s1)
t = w.factory('FormulaVar::t("(x - hm) / hs", {x, hm[0,-5,5], hs[1,0.1,3]})')
hist = k.createHistogram('x', 10000)
dh = DataHist('dh', 'dh', ArgList(x), hist)
h = HistPdf('h', 'h', ArgList(t), ArgList(x), dh, 2)
## 5. Plot results
f1 = x.frame()
f1.SetTitle('Training Data')
data1.plotOn(f1)
g.plotOn(f1)
k.plotOn(f1, LineColor(kRed), LineStyle(kDashed))
h.plotOn(f1, LineColor(kBlack), LineStyle(kDashed))
canvases.next('Training_Data')
f1.Draw()
## 6. Fit h1 to data2 to simulate a "measurement" of m2, s2.
h.fitTo(data2)
f2 = x.frame()
def getMistagBinBounds(config, mistag, mistagdistrib):
"""
suggest a binning for turning per-event mistag into mistag categories
This routine takes a mistag observable and a PDF or data set, and suggests
a number of mistag category boundaries which have approximately equal
statistics in each category.
config -- config dictionary
mistag -- mistag observable (eta)
mistagdistrib -- a PDF or a RooDataSet
returns a (python) list of mistag category bin bounds
relevant configuration dictionary keys:
'NMistagCategories':
number of mistag categories for which to suggest a set of bin bounds
"""
mistag.setBins(1000, 'MistagBinBounds')
from ROOT import RooArgSet, RooHistPdf, RooDataHist
if (mistagdistrib.InheritsFrom('RooAbsData') and not
mistagdistrib.InheritsFrom('RooDataHist')):
# ok, unbinned data set, get only tagged events, and form a binned clone
argset = RooArgSet(mistag)
mistagdistrib = mistagdistrib.reduce(
RooFit.SelectVars(argset), RooFit.cut('0 != qt'))
ROOT.SetOwnership(mistagdistrib, True)
dhist = RooDataHist(
'%s_binned' % mistagdistrib.GetName(),
'%s_binned' % mistagdistrib.GetName(),
mistagdistrib.get(), 'MistagBinBounds')
dhist.add(mistagdistrib)
mistagdistrib = dhist
if mistagdistrib.InheritsFrom('RooAbsData'):
# convert a binned dataset to a RooHistPdf
dhist = mistagdistrib
mistagdistrib = RooHistPdf('%s_pdf' % dhist.GetName(),
'%s_pdf' % dhist.GetName(), RooArgSet(mistag), dhist)
if (mistagdistrib.InheritsFrom('RooAbsPdf')):
# use createCdf to obtain the CDF
cdfroofit = mistagdistrib.createCdf(
RooArgSet(mistag), RooArgSet(mistag))
ROOT.SetOwnership(cdfroofit, True)
def cdf(x):
oldval = mistag.getVal()
mistag.setVal(x)
retVal = cdfroofit.getVal()
mistag.setVal(oldval)
return retVal
if (mistagdistrib.InheritsFrom('RooHistPdf') and
(abs(cdf(mistag.getMin())) > 1e-9 or
abs(cdf(mistag.getMax()) - 1.) > 1e-9)):
# createCdf does not work properly for RooHistPdf in older ROOT
# versions because RooHistPdf does not support integrals over
# subranges, so we have to fake this functionality until it's
# supported by RooFit upstream
#
# capture histogram bin boundaries and contents
print 'WARNING: Your version of RooFit still has buggy analytical ' \
'integrals for RooHistPdf - activating workaround.'
binboundlist = mistagdistrib.binBoundaries(
mistag, mistag.getMin(), mistag.getMax())
ROOT.SetOwnership(binboundlist, True)
binbounds = [ v for v in binboundlist ]
del binboundlist
bincontents = [ ]
oldval = mistag.getVal()
for i in xrange(0, len(binbounds) - 1):
mistag.setVal(0.5 * (binbounds[i] + binbounds[i + 1]))
bincontents.append(mistagdistrib.getValV(RooArgSet(mistag)))
mistag.setVal(oldval)
# build CDF from histogram
def cdf(x):
s = 0.
for i in xrange(0, len(binbounds) - 1):
if x < binbounds[i]:
break
elif x >= binbounds[i + 1]:
s += bincontents[i]
else:
s += (bincontents[i] * (x - binbounds[i]) /
(binbounds[i + 1] - binbounds[i]))
break
return s
# find x for which f(x) = y by bisection
def mybisect(y, f, lo, hi):
initdx = abs(hi - lo)
flo, fhi = f(lo) - y, f(hi) - y
if 0. == flo: return lo
elif 0. == fhi: return hi
mid = .5 * (lo + hi)
while (abs(hi - lo) > 1e-15 and abs(hi - lo) / initdx > 1e-15):
fmid = f(mid) - y
if 0. == fmid: break
elif flo * fmid < 0.: hi, fhi = mid, fmid
elif fmid * fhi < 0.: lo, flo = mid, fmid
else: raise ValueError('no sign change in f(x) between %g and %g'
% (lo, hi))
mid = .5 * (lo + hi)
return mid
# find binning with roughly same stats by inverting the CDF by bisection
lo, hi, binsum = mistag.getMin(), mistag.getMax(), cdf(mistag.getMax())
retVal = [ lo ]
for i in xrange(1, config['NMistagCategories']):
retVal.append(mybisect(binsum *
float(i) / float(config['NMistagCategories']), cdf, lo, hi))
retVal.append(hi)
print 'INFO: suggested mistag category bounds: %s' % str(retVal)
return retVal
args.signal_model, int(mass))
# get signal shape
hSig = signal_shapes_file.Get("h_" + args.signal_model + "_" +
str(int(mass)))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
print "[debug] hSig.Integral() = " + str(hSig.Integral())
print "[debug] hSig.GetMean() = " + str(hSig.GetMean())
print "[debug] hSig.GetRMS() = " + str(hSig.GetRMS())
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(mjj),
hSig)
rooSigHist.Print()
signal = RooHistPdf('signal', 'signal', RooArgSet(mjj), rooSigHist)
signal.Print()
signal_norm = RooRealVar('signal_norm', 'signal_norm', 0, -1e+05,
1e+05)
if args.fitBonly: signal_norm.setConstant()
signal_norm.Print()
p1 = RooRealVar('p1', 'p1', args.p1, 0., 100.)
p2 = RooRealVar('p2', 'p2', args.p2, 0., 60.)
p3 = RooRealVar('p3', 'p3', args.p3, -10., 10.)
if args.fixP3: p3.setConstant()
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p1, p2, p3))
nqcd = RooRealVar("nqcd", "number of qcd events", n_qcd, n_qcd, n_qcd)
#k2nqcd = RooFormulaVar("k2nqcd","number of qcd events"," ",RooArgList(k2,nqcd))
data = RooDataHist("data", "data set with (x)", RooArgList(x), h_data_m3HistS)
ttbar = RooDataHist("ttbar", "data set with (x)", RooArgList(x),
h_ttbar_m3HistS)
background = RooDataHist("background", "data set with (x)", RooArgList(x),
h_background_m3Hist)
qcd = RooDataHist("qcd", "data set with (x)", RooArgList(x),
h_qcd_data_m3HistS)
# RooHistPdf::RooHistPdf(const char* name, const char* title, const RooArgSet& vars, const RooDataHist& dhist, int intOrder = 0) =>
#print "ttbar type: "+str(type(ttbar))
#print "rooArglist(x):"+str(type(RooArgList(x)))
ttbarpdf = RooHistPdf("ttbarpdf", "ttbarpdf", RooArgList(x), RooArgList(x),
ttbar)
backgroundpdf = RooHistPdf("backgroundpdf", "backgroundpdf", RooArgList(x),
RooArgList(x), background)
qcdpdf = RooHistPdf("qcdpdf", "qcdpdf", RooArgList(x), RooArgList(x), qcd)
#ttbarpdf = RooHistPdf(ttbar,"ttbarpdf")
#backgroundpdf = RooHistPdf(background,"backgroundpdf")
model = RooAddPdf("model", "model", RooArgList(ttbarpdf, backgroundpdf),
RooArgList(k1))
model2 = RooAddPdf("model2", "model2", RooArgList(model), RooArgList(k2nmc))
model3 = RooAddPdf("model3", "model3", RooArgList(model2, qcdpdf),
RooArgList(k2nmc, nqcd))
#model3 = RooAddPdf("model3", "model3",RooArgList( model, qcdpdf), RooArgList(k2nmc,k2nqcd))
model3.fitTo(data)
def morph(hist1, hist2, mass1, mass2, targetMass, debug = False):
from ROOT import gROOT, RooRealVar, RooIntegralMorph,\
kRed,kBlue,kViolet, RooDataHist, RooArgSet,\
RooArgList, RooHistPdf, RooAbsReal, RooFit
import re
if mass1<mass2:
histLow = hist1
massLow = mass1
histHigh = hist2
massHigh = mass2
else:
histLow = hist2
massLow = mass2
histHigh = hist1
massHigh = mass1
x = RooRealVar("x", "x", histLow.GetXaxis().GetXmin(), histLow.GetXaxis().GetXmax())
mAlpha = 1.0 - float(targetMass-massLow)/float(massHigh-massLow)
print 'alpha:',mAlpha,
newIntegral = histLow.Integral() + \
(targetMass - massLow) * \
(histHigh.Integral()-histLow.Integral()) / \
(massHigh-massLow)
print 'low integral:', histLow.Integral(), \
'high integral:', histHigh.Integral(), \
'new integral:', newIntegral
newHistLow = RooDataHist("sigHistLow_hist", "sigHistLow_hist", RooArgList(x), histLow);
sigHistLow = RooHistPdf("sigHistLow", "sigHistLow", RooArgList(x),
RooArgList(x), newHistLow);
newHistHigh = RooDataHist("sigHistHigh_hist", "sigHistHigh_hist", RooArgList(x), histHigh);
sigHistHigh = RooHistPdf("sigHistHigh", "sigHistHigh", RooArgList(x),
RooArgList(x), newHistHigh);
#x.Print("v")
x.setBins( histLow.GetNbinsX(), "cache")
alpha_morph = RooRealVar("alpha_morph", "#alpha_{morph}", mAlpha, 0., 1.)
alpha_morph.setBins(10,"cache") ;
sigModel = RooIntegralMorph("sigModel", "sigModel", sigHistLow,
sigHistHigh, x, alpha_morph)
if (debug):
from ROOT import gPad, RooFit
frame = x.frame()
sigHistLow.plotOn(frame, RooFit.LineColor(kRed+1),
RooFit.LineStyle(2))
sigHistHigh.plotOn(frame, RooFit.LineColor(kBlue+1),
RooFit.LineStyle(2))
sigModel.plotOn(frame, RooFit.LineColor(kViolet+1),
RooFit.LineStyle(9))
frame.Draw()
gPad.Update()
gPad.WaitPrimitive()
morphHist = sigModel.createHistogram(re.sub(r'\d+', '%i' % targetMass,
hist1.GetName()), x, RooFit.Binning(histLow.GetNbinsX()))
morphHist.Scale(newIntegral/morphHist.Integral())
morphHist.SetName(re.sub(r'\d+', '%i' % targetMass, hist1.GetName()))
# if debug:
# morphHist.Print()
return morphHist
## mmgMass.setRange(mmgMass.getMin() - mmgMassRangeMean,
## mmgMass.getMax() - mmgMassRangeMean)
phoEResFuncOfMMGMassShifted = w.factory(
"""FormulaVar::phoEResFuncOfMMGMassShifted(
"(mmgMassShifted - phoMean)/phoWidth + ({mcscale})",
{{mmgMassShifted, phoMean, phoWidth}}
)""".format(
mcscale=phoEScaleMC
)
)
# phoEResFuncOfMMGMass = w.factory('FormulaVar::phoEResFuncOfMMGMass2("mmgMass ", {mmgMass})')
## Plug in the mass smering mean and width into the smearing shape
phoSmear = RooHistPdf(
"phoSmear", "phoSmear", RooArgList(phoEResFuncOfMMGMassShifted), RooArgList(phoERes), phoEResDataHist, 2
)
## TODO: use RooCustomizer to transform phoEResShape
## Define the transform such that the phoEResShape is always evaluated
## inside of it's boundaries.
cust = RooCustomizer(phoEResShape, "transformed")
cust.replaceArg(phoERes, phoEResFuncOfMMGMassShifted)
phoSmear2 = cust.build()
w.Import(phoSmear)
## Apply photon smearing to the theory
mmgMassShifted.setRange(-30, 30)
mmgMassShifted.setBins(10000, "fft")
theoryXphoSmear = w.factory("FCONV::theoryXphoSmear(mmgMassShifted, phoSmear, theory)")
## mmgMassPhoGenEFunc = w.factory('FormulaVar::mmgMassPhoGenEFunc("mmgMass", {mmgMass})')
def fit(self):
fit_variable = RooRealVar("fit_variable", "fit_variable",
self.fit_boundaries[0],
self.fit_boundaries[1])
fit_variable.setBins(self.histograms[self.data_label].nbins())
variables = RooArgList()
variables.add(fit_variable)
variable_set = RooArgSet()
variable_set.add(fit_variable)
roofit_histograms = {}
roofit_pdfs = {}
roofit_variables = {}
N_min = 0.
N_max = self.normalisation[self.data_label] * 2.
pdf_arglist = RooArgList()
variable_arglist = RooArgList()
roofit_histograms[self.data_label] = RooDataHist(
self.data_label, self.data_label, variables,
self.histograms[self.data_label])
for sample in self.samples:
roofit_histogram = RooDataHist(sample, sample, variables,
self.histograms[sample])
roofit_histograms[sample] = roofit_histogram
roofit_pdf = RooHistPdf('pdf' + sample, 'pdf' + sample,
variable_set, roofit_histogram)
roofit_pdfs[sample] = roofit_pdf
roofit_variable = RooRealVar(sample, sample + " events",
self.normalisation[sample], N_min,
N_max)
roofit_variables[sample] = roofit_variable
pdf_arglist.add(roofit_pdf)
variable_arglist.add(roofit_variable)
model = RooAddPdf('model', 'sum of all known', pdf_arglist,
variable_arglist)
use_model = model
if self.constraints:
arg_set = RooArgSet(model)
constraints = self.get_fit_normalisation_constraints(
model, roofit_variables)
for constraint in constraints:
arg_set.add(constraint)
model_with_constraints = RooProdPdf(
"model_with_constraints", "model with gaussian constraints",
arg_set, RooLinkedList())
use_model = model_with_constraints
if self.method == 'TMinuit':
#WARNING: number of cores changes the results!!!
self.saved_result = use_model.fitTo(
roofit_histograms[self.data_label],
RooFit.Minimizer("Minuit2", "Migrad"),
RooFit.NumCPU(1),
RooFit.Extended(),
RooFit.Save(),
)
results = {}
for sample in self.samples:
results[sample] = (roofit_variables[sample].getVal(),
roofit_variables[sample].getError())
self.results = results
## Shift the mass range to be symmetric around zero
## mmgMassRangeMean = 0.5 * (mmgMass.getMin() + mmgMass.getMax())
## mmgMass.setRange(mmgMass.getMin() - mmgMassRangeMean,
## mmgMass.getMax() - mmgMassRangeMean)
phoEResFuncOfMMGMassShifted = w.factory(
'''FormulaVar::phoEResFuncOfMMGMassShifted(
"(mmgMassShifted - phoMean)/phoWidth + ({mcscale})",
{{mmgMassShifted, phoMean, phoWidth}}
)'''.format(mcscale = phoEScaleMC))
#phoEResFuncOfMMGMass = w.factory('FormulaVar::phoEResFuncOfMMGMass2("mmgMass ", {mmgMass})')
## Plug in the mass smering mean and width into the smearing shape
phoSmear = RooHistPdf('phoSmear', 'phoSmear',
RooArgList(phoEResFuncOfMMGMassShifted),
RooArgList(phoERes), phoEResDataHist, 2)
w.Import(phoSmear)
## Apply photon smearing to the theory
mmgMassShifted.setRange(-30, 30)
mmgMassShifted.setBins(10000, 'fft')
theoryXphoSmear = w.factory('FCONV::theoryXphoSmear(mmgMassShifted, phoSmear, theory)')
## mmgMassPhoGenEFunc = w.factory('FormulaVar::mmgMassPhoGenEFunc("mmgMass", {mmgMass})')
## theoryXphoSmear = RooFFTConvPdf('theoryXphoSmear', 'theoryXphoSmear',
## mmgMassPhoGenEFunc, mmgMassPhoGenE, theory, phoSmear)
## Fit Smeared theory to data
#theoryXphoSmear.fitTo(mmgData, Range(70,110))
def change_dataset_to_histpdf(workspace,x,dataset): datahist = dataset.binnedClone(dataset.GetName()+"_binnedClone",dataset.GetName()+"_binnedClone") histpdf = RooHistPdf(dataset.GetName()+"_histpdf",dataset.GetName()+"_histpdf",RooArgSet(x),datahist) getattr(workspace,'import')(histpdf)
def shapeCards( process, isData, datahistosFile, histosFile, signalHistosFile, signalSample, hist, signalMass, minMass, maxMass, jesValue, jerValue, lumiUnc, outputName ):
"""function to run Roofit and save workspace for RooStats"""
warnings.filterwarnings( action='ignore', category=RuntimeWarning, message='.*class stack<RooAbsArg\*,deque<RooAbsArg\*> >' )
hSignal = signalHistosFile.Get(hist+'_'+signalSample)
hSignal.Rebin(10)
htmpSignal = hSignal.Clone()
#htmpSignal.Scale(100)
signalXS = search(dictXS, 'RPVStopStopToJets_UDD312_M-'+str(signalMass) )
#hSignal.Scale( lumi*signalXS / hSignal.Integral())
massAve = RooRealVar( 'massAve', 'massAve', minMass, maxMass )
rooSigHist = RooDataHist( 'rooSigHist', 'rooSigHist', RooArgList(massAve), hSignal )
rooSigHist.Print()
signal = RooHistPdf('signal','signal',RooArgSet(massAve),rooSigHist)
signal.Print()
signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+04,1e+04)
#if args.fitBonly: signal_norm.setConstant()
signal_norm.Print()
hBkg = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
#hBkg = histosFile.Get(hist+'_QCDPtAll_BCD')
bkgAcc = round(hBkg.Integral( hBkg.GetXaxis().FindBin( minMass ), hBkg.GetXaxis().FindBin( maxMass )))
#hBkg.Scale(1/hBkg.Integral())
hPseudo = hBkg.Clone()
hPseudo.Reset()
#background_norm = RooRealVar('background_norm','background_norm',bkgAcc,0.,1e+07)
background_norm = RooRealVar('background_norm','background_norm',1.,0.,1e+07)
background_norm.Print()
if 'template' in process:
rooBkgHist = RooDataHist( 'rooBkgHist', 'rooBkgHist', RooArgList(massAve), hBkg )
rooBkgHist.Print()
background = RooHistPdf('background','background',RooArgSet(massAve),rooBkgHist)
background.Print()
else:
p1 = RooRealVar('p1','p1', 1 ,0.,100.)
p2 = RooRealVar('p2','p2', 1 ,0.,60.)
p3 = RooRealVar('p3','p3', 1 , -10.,10.)
background = RooGenericPdf('background','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(1300,1300,1300),RooArgList(massAve,p1,p2,p3))
background.Print()
### S+B model
if not isData:
newNumEvents = random.randint( bkgAcc-round(TMath.Sqrt(bkgAcc)), bkgAcc+round(TMath.Sqrt(bkgAcc)) )
print 'Events in MC:', bkgAcc, ', in PseudoExperiment:', newNumEvents
hPseudo.FillRandom( hBkg, newNumEvents )
#hPseudo.Scale(1/hPseudo.Integral())
#hData = histosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_ABCDProj')
hData = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
#hData = histosFile.Get(hist+'_QCDPtAll_A')
#hData.Add(htmpSignal)
#hData.Scale(1/hData.Integral())
rooDataHist = RooDataHist('rooDatahist','rooDatahist',RooArgList(massAve), hData if isData else hPseudo )
rooDataHist.Print()
#model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
#res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(0))
#res.Print()
############# JES and JER uncertainties
hSigSyst = {}
hSigSystDataHist = {}
signalCDF = TGraph(hSignal.GetNbinsX()+1)
# JES and JER uncertainties
if args.jesUnc or args.jerUnc:
signalCDF.SetPoint(0,0.,0.)
integral = 0.
for i in range(1, hSignal.GetNbinsX()+1):
x = hSignal.GetXaxis().GetBinLowEdge(i+1)
integral = integral + hSignal.GetBinContent(i)
signalCDF.SetPoint(i,x,integral)
if args.jesUnc:
print ' |---> Adding JES'
hSigSyst['JESUp'] = hSignal.Clone()
hSigSyst['JESDown'] = hSignal.Clone()
if args.jerUnc:
print ' |---> Adding JER'
hSigSyst['JERUp'] = hSignal.Clone()
hSigSyst['JERDown'] = hSignal.Clone()
# reset signal histograms
for key in hSigSyst:
hSigSyst[key].Reset()
hSigSyst[key].SetName(hSigSyst[key].GetName() + '_' + key)
# produce JES signal shapes
if args.jesUnc:
for q in range(1, hSignal.GetNbinsX()+1):
xLow = hSignal.GetXaxis().GetBinLowEdge(q)
xUp = hSignal.GetXaxis().GetBinLowEdge(q+1)
jes = 1. - jesValue
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSigSyst['JESUp'].SetBinContent(q, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
jes = 1. + jesValue
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSigSyst['JESDown'].SetBinContent(q, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
hSigSystDataHist['JESUp'] = RooDataHist('hSignalJESUp','hSignalJESUp',RooArgList(massAve),hSigSyst['JESUp'])
hSigSystDataHist['JESDown'] = RooDataHist('hSignalJESDown','hSignalJESDown',RooArgList(massAve),hSigSyst['JESDown'])
# produce JER signal shapes
if args.jerUnc:
for i in range(1, hSignal.GetNbinsX()+1):
xLow = hSignal.GetXaxis().GetBinLowEdge(i)
xUp = hSignal.GetXaxis().GetBinLowEdge(i+1)
jer = 1. - jerValue
xLowPrime = jer*(xLow-float(signalMass))+float(signalMass)
xUpPrime = jer*(xUp-float(signalMass))+float(signalMass)
hSigSyst['JERUp'].SetBinContent(i, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
jer = 1. + jerValue
xLowPrime = jer*(xLow-float(signalMass))+float(signalMass)
xUpPrime = jer*(xUp-float(signalMass))+float(signalMass)
hSigSyst['JERDown'].SetBinContent(i, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
hSigSystDataHist['JERUp'] = RooDataHist('hSignalJERUp','hSignalJERUp',RooArgList(massAve),hSigSyst['JERUp'])
hSigSystDataHist['JERDown'] = RooDataHist('hSignalJERDown','hSignalJERDown',RooArgList(massAve),hSigSyst['JERDown'])
myWS = RooWorkspace("myWS")
getattr(myWS,'import')(rooSigHist,RooFit.Rename("signal"))
getattr(myWS,'import')(rooBkgHist,RooFit.Rename("background"))
#getattr(myWS,'import')(signal_norm)
getattr(myWS,'import')(background_norm)
if args.jesUnc:
getattr(myWS,'import')(hSigSystDataHist['JESUp'],RooFit.Rename("signal__JESUp"))
getattr(myWS,'import')(hSigSystDataHist['JESDown'],RooFit.Rename("signal__JESDown"))
if args.jerUnc:
getattr(myWS,'import')(hSigSystDataHist['JERUp'],RooFit.Rename("signal__JERUp"))
getattr(myWS,'import')(hSigSystDataHist['JERDown'],RooFit.Rename("signal__JERDown"))
getattr(myWS,'import')(rooDataHist,RooFit.Rename("data_obs"))
myWS.Print()
outputRootFile = currentDir+'/Rootfiles/workspace_'+outputName+'.root'
myWS.writeToFile(outputRootFile, True)
print ' |----> Workspace created in root file:\n', outputRootFile
# -----------------------------------------
# write a datacard
dataCardName = currentDir+'/Datacards/datacard_'+outputName+'.txt'
datacard = open( dataCardName ,'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc or args.jerUnc or args.lumiUnc or args.normUnc or args.unc:
datacard.write('shapes * * '+outputRootFile+' myWS:$PROCESS myWS:$PROCESS__$SYSTEMATIC\n')
else: datacard.write("shapes * * "+outputRootFile+" myWS:$PROCESS \n")
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
datacard.write('rate -1 -1\n')
datacard.write('------------------------------\n')
if args.lumiUnc: datacard.write('lumi lnN %f -\n'%(lumiUnc))
if args.jesUnc: datacard.write('JES shape 1 -\n')
if args.jerUnc: datacard.write('JER shape 1 -\n')
#flat parameters --- flat prior
if args.normUnc: datacard.write('background_norm flatParam\n')
#datacard.write('p1 flatParam\n')
datacard.close()
print ' |----> Datacard created:\n', dataCardName
def main():
# Parse all command line arguments using the argparse module.
parser = argparse.ArgumentParser(description='PyRoot analysis demostrating the us of a DST.')
parser.add_argument("dst_file", help="ROOT DST file to process")
parser.add_argument("-o", "--output", help="Name of output pdf file")
parser.add_argument("-m", "--mc", help="is MonteCarlo")
parser.add_argument("-p", "--pulser", help="is Pulser")
args = parser.parse_args()
# If an output file name was not specified, set a default name and warn
# the user
if args.output:
output_file = args.output
else:
output_file = "analysis_output.root"
print "[ HPS ANALYSIS ]: An output file name was not specified. Setting the name to "
print output_file
print "[ HPS ANALYSIS ]: Output file is "+output_file
isMC=False
if args.mc:
print "[ HPS ANALYSIS ]: Setting to run as MC"
isMC=True
isPulser=False
if args.pulser:
print "[ HPS ANALYSIS ]: Setting to run from a pulser file"
isPulser=True
#################################
# Event Selection
################################
#clean up event first
#### nominal selection
nTrkMax=10
nTrkMin=2
nPosMax=3
###### two tracks (e+/e-) exactly
# nTrkMax=2
# nTrkMin=2
# nPosMax=1
###### more than 1 electron
# nTrkMax=10
# nTrkMin=3
# nPosMax=1
###################
#v0 cuts
v0Chi2=10
#ESum -- full region
v0PzMax=1.2
v0PzMin=0.55
#ESum -- Radiative region
# v0PzMax=1.2
# v0PzMin=0.80
v0PyMax=0.2 #absolute value
v0PxMax=0.2 #absolute value
v0VzMax=25.0# mm from target
v0VyMax=1.0# mm from target
v0VxMax=2.0# mm from target
# track quality cuts
trkChi2=10
beamCut=0.8
minPCut=0.05
trkPyMax=0.2
trkPxMax=0.2
# slopeCut=0.03
slopeCut=0.0
trkDeltaT=4#ns
cluDeltaT=2#ns
cluTrkDeltaT=4#ns
##############
# ESum slices; upper limits
nSlicesESum=5
esumMin=0.55
esumMax=1.2
sliceSizeESum=0.1 #100MeV starting at esumMin
##############
trackKiller=False
tkThreshold=0.5 #GeV, below this start killing tracks
tkThreshEff=1.0
tkLowPoint=0.20
tkLowPointEff=0.40
# tkSlope=2.6
# tkIntercept=-0.04
#calculate tkSlope and Intercept
tkSlope=(tkThreshEff-tkLowPointEff)/(tkThreshold-tkLowPoint)
tkIntercept=tkThreshEff-tkSlope*tkThreshold
##############
requireECalMatch = True
requireECalFiducial = False
requireECalSuperFiducial = True
useGBL=True
# Open the ROOT file
# root_file = ROOT.TFile(str(args.dst_file))
# Get the TTree "HPS_EVENT" containing the HpsEvent branch and all
# other colletions
# tree = root_file.Get("HPS_Event")
#use a TChain
print "[ HPS ANALYSIS ]: Reading in root chain from "+args.dst_file
tree=ROOT.TChain("HPS_Event")
tree.Add(str(args.dst_file)+"*")
# Create an HpsEvent object in order to read the TClonesArray
# collections
hps_event = HpsEvent()
b_hps_event = tree.SetBranchAddress("Event", ROOT.AddressOf(hps_event))
#--- Analysis ---#
#----------------#
#counters
nEvents=0;
nPassBasicCuts=0;
nPassV0Cuts=0;
nPassTrkCuts=0;
nPassNCand=0
nPassECalMatch=0;
nFakeTri=0
if 1==0 :
extraElectronProb=0.0
dataNele=170.0;
btNele=1.4;
hf=ROOT.TFile("OutputHistograms/Data/hps_005772_pass6_useGBL_ECalMatch_SuperFiducialCut.root")
hfMC=ROOT.TFile("OutputHistograms/MC/beam-tri_HPS-EngRun2015-Nominal-v4-4_pass6_useGBL_ECalMatch_SuperFiducialCut.root")
pele=ROOT.RooRealVar("pele","pele",0,1)
peleHist=hf.Get("raweleMom")
peleMCHist=hfMC.Get("raweleMom")
peleHist.Scale(1/dataNele)
peleMCHist.Scale(1/btNele)
peleHist.Add(peleMCHist,-1.0) #subtract the MC from the data to get the distribution of extra tracks...this is cheating!
for i in xrange(0,peleHist.GetNbinsX()) :
print "Electron Momentum bin i = " +str( peleHist.GetBinContent(i) )
if peleHist.GetBinContent(i) <0 :
peleHist.SetBinContent(i,0)
peleHist.Print("V")
peleDH=RooDataHist("peleDH","peleDH",ROOT.RooArgList(pele),peleHist)
peleDH.Print("V")
extraElectronPdf=RooHistPdf("extraElectronPdf","extraElectronPdf",ROOT.RooArgSet(pele),peleDH)
print 'pdf is made...printing info'
extraElectronPdf.Print("V")
# if isMC and random.random()<extraElectronProb :
#add an extra electron based on electron momentum
print 'generating events'
newElePData=extraElectronPdf.generate(ROOT.RooArgSet(pele),1000000.0,True, False)
newElePData.Print("V")
seedCnt=0
# Loop over all events in the file
for entry in xrange(0, tree.GetEntries()) :
# Print the event number every 500 events
if (entry+1)%10000 == 0 : print "Event " + str(entry+1)
tree.GetEntry(entry)
if not hps_event.isPair1Trigger() and not isMC and not isPulser: continue
nEvents+=1
addFakeEle=False
if 1==0 :
if isMC and random.random()<extraElectronProb :
#add an extra electron based on electron momentum
addFakeEle=True
newEleP=newElePData.get(seedCnt).find("pele").getVal()
seedCnt=seedCnt+1
# print 'Inserting an electron with momentum = '+str(newEleP)
# newEleCluster=hps_event.addEcalCluster();
# newEleTrack=hps_event.addTrack();
# print 'numbe of HpsParticles before = '+str(hps_event.getNumberOfParticles(HpsParticle.FINAL_STATE_PARTICLE))
# newEle=hps_event.addParticle(HpsParticle.FINAL_STATE_PARTICLE)
# print 'numbe of HpsParticles after = '+str(hps_event.getNumberOfParticles(HpsParticle.FINAL_STATE_PARTICLE))
newEle=HpsParticle()
newEle.setCharge(-1)
newEle.setPDG(11)
newEle.setEnergy(newEleP)
sign=1
if random.random()<0.5 :
sign=-1
newEleMom=[math.sin(0.03)*newEleP,sign*math.sin(0.04)*newEleP,math.cos(0.04)*newEleP]
newEle.setMomentum(np.array(newEleMom))
#fake track info
newEleTrack=SvtTrack()
newEleTrack.setTrackParameters(0.0,0.03,0.0001,sign*0.04,0.0)
newEleTrack.setTrackTime(40.0)
newEleTrack.setParticle(newEle)
newEleTrack.setChi2(3.0)
newEleTrack.setPositionAtEcal(np.array([300.0,sign*50.0,1350.0]))
#fake cluster info
newEleCluster=EcalCluster()
newEleCluster.setEnergy(newEleP)
foobar=[300.0,sign*50.0,1350.0]
newEleCluster.setPosition(np.array(foobar,dtype='float32'))
newEle.addTrack(newEleTrack)
newEle.addCluster(newEleCluster)
# print 'fake electron cluster x ' + str(newEle.getClusters().First().getPosition()[0])
# Loop over all tracks in the event
npositrons=0
n_tracks=0
for track_n in xrange(0, hps_event.getNumberOfTracks()) :
track = hps_event.getTrack(track_n)
if track is None :
continue
# if useGBL and track.getParticle().getType()<32 : continue
# if not useGBL and track.getParticle().getType()>31 : continue
if trkMatchAndFiducial(track.getParticle()) and trkMomentum(track,minPCut,beamCut): # count only matched tracks in defined fiducial region
n_tracks+=1
if track.getCharge()>0 :
npositrons+=1
myhist.rawposMom.Fill(pMag(track.getMomentum()))
else :
myhist.raweleMom.Fill(pMag(track.getMomentum()))
# findWABPair(track.getParticle(),hps_event)
if addFakeEle :
myhist.raweleMom.Fill(newEle.getEnergy())
n_tracks=n_tracks+1
# print "nTracks = "+str(n_tracks)+"; nPositrons = "+str(npositrons)
# if n_tracks/2.0>nTrkMax : continue #do this very dumb thing (divide by 2 to un-double count GBL tracks)
# if n_tracks/2.0<2: continue
myhist.nTrk.Fill(n_tracks);
myhist.nPos.Fill(npositrons);
myhist.nEle.Fill(n_tracks-npositrons);
myhist.nClust.Fill(hps_event.getNumberOfEcalClusters())
if n_tracks>nTrkMax : continue
if n_tracks<nTrkMin: continue
if npositrons<1 or npositrons>nPosMax : continue
nPassBasicCuts+=1
# print "passed basic cuts"
candidateList=[]
bestCandidate=-99
nCandidate=0
# loop over all v0 candidates...
for uc_index in xrange(0, hps_event.getNumberOfParticles(HpsParticle.UC_V0_CANDIDATE)):
particle = hps_event.getParticle(HpsParticle.UC_V0_CANDIDATE, uc_index)
if useGBL and particle.getType()<32 : continue
if not useGBL and particle.getType()>31 : continue
# print "found one..."
vchi2=particle.getVertexFitChi2();
vposition=particle.getVertexPosition();
vmomentum=particle.getMomentum();
if vchi2>v0Chi2 : continue
# use the measured sum of momentum
# if vmomentum[2]>v0PzMax : continue
# if vmomentum[2]<v0PzMin : continue
#recon'ed vertex position cuts
if abs(vposition[0])>v0VxMax : continue
if abs(vposition[1])>v0VyMax :continue
# if abs(vposition[2])>v0VzMax :continue
# Only look at particles that have two daugther particles...
daughter_particles = particle.getParticles()
if daughter_particles.GetSize() != 2 : continue
# Only look at particles that are composed of e+e- pairs
if daughter_particles.At(0).getCharge()*daughter_particles.At(1).getCharge() > 0 : continue
# print "Passed daughter number cuts"
electron = daughter_particles.At(0)
positron = daughter_particles.At(1)
if daughter_particles.At(0).getCharge()>0:
electron = daughter_particles.At(1)
positron = daughter_particles.At(0)
pEle=electron.getMomentum()
pPos=positron.getMomentum()
v0Sum=pMag(pSum(pEle,pPos))
#total momentum sum cuts
if v0Sum>v0PzMax : continue
if v0Sum<v0PzMin : continue
nPassV0Cuts+=1
print "Passed v0 cuts"
############# tracking cuts
#momentum cuts...get rid of very soft or very hard tracks
if pMag(pEle)>beamCut or pMag(pPos)>beamCut : continue
if pMag(pEle)<minPCut or pMag(pPos)<minPCut : continue
#top+bottom requirement
if pEle[1]*pPos[1]>0 : continue
print 'looking at tracks now'
print len(electron.getTracks())
if len(electron.getTracks()) == 0 or len(positron.getTracks()) == 0: continue
eleTrk=electron.getTracks().At(0)
posTrk=positron.getTracks().At(0)
if eleTrk is None or posTrk is None : continue
eleTrk.Print("v")
#track timing
if eleTrk.getTrackTime() - posTrk.getTrackTime()> trkDeltaT :
continue
#track slope (if any cut)
if abs(eleTrk.getTanLambda())<slopeCut or abs(posTrk.getTanLambda())<slopeCut :
continue
print 'satisfied timing cuts...'
##############
# track killer part
if isMC and trackKiller :
if pMag(pEle) <tkThreshold : #electron
tkEff=tkSlope*pMag(pEle)+tkIntercept
if random.random()>tkEff :
continue
elif random.random()>tkThreshEff : #allow for a flat killer above threshold
continue
if pMag(pPos) <tkThreshold : # positron
tkEff=tkSlope*pMag(pPos)+tkIntercept
if random.random()>tkEff :
continue
elif random.random()>tkThreshEff : #allow for a flat killer above threshold
continue
# end of track killer
##############
nPassTrkCuts+=1
##############
# ECAL matching and timing cuts...also fiducial region cuts...
if requireECalMatch:
if positron.getClusters().GetEntries() == 0 :
continue
if electron.getClusters().GetEntries() == 0 :
continue
posCluster=positron.getClusters().First()
eleCluster=electron.getClusters().First()
if eleCluster.getClusterTime()- posCluster.getClusterTime() > cluDeltaT:
continue
if eleTrk.getTrackTime() - eleCluster.getClusterTime()+43.5 > cluTrkDeltaT :
continue
if posTrk.getTrackTime() - posCluster.getClusterTime()+43.5 > cluTrkDeltaT :
continue
if requireECalFiducial:
#ANTI-fiducial cut
# if myhist.inFiducialRegion(posCluster.getPosition()[0],posCluster.getPosition()[1]) :
# continue
# if myhist.inFiducialRegion(eleCluster.getPosition()[0],eleCluster.getPosition()[1]) :
# continue
#Fiducial cut
if not myhist.inFiducialRegion(posCluster.getPosition()[0],posCluster.getPosition()[1]) :
continue
if not myhist.inFiducialRegion(eleCluster.getPosition()[0],eleCluster.getPosition()[1]) :
continue
if requireECalSuperFiducial :
if not myhist.inSuperFiducialRegion(posCluster.getPosition()[0],posCluster.getPosition()[1]) :
continue
if not myhist.inSuperFiducialRegion(eleCluster.getPosition()[0],eleCluster.getPosition()[1]) :
continue
nPassECalMatch+=1
##############
#Passed the cuts..append the candidate index
findWABPair(electron,hps_event)
candidateList.append(uc_index)
numCands=len(candidateList)
if addFakeEle :
for track_n in xrange(0, hps_event.getNumberOfTracks()) :
track = hps_event.getTrack(track_n)
if track is None :
continue
if trkMatchAndFiducial(track.getParticle()) and trkMomentum(track,minPCut,beamCut) and track.getCharge>0 and newEle.getMomentum()[1]*track.getMomentum()[1]<0: # get positron in fudicial region; make sure it's in opposite quadrant
myhist.eSum.Fill(newEle.getEnergy()+pMag(track.getMomentum()))
numCands+=1
if len(candidateList) == 0 :
print 'made a new trident event'
nFakeTri+=1
myhist.nCand.Fill(numCands)
#########################
# found some candidates...lets fill plots...
#########################
for index in range(0,len(candidateList)) :
particle = hps_event.getParticle(HpsParticle.TC_V0_CANDIDATE, candidateList[index])
myhist.fillCandidateHistograms(particle)
myhist.nTrkCand.Fill(n_tracks);
myhist.nPosCand.Fill(npositrons);
myhist.nEleCand.Fill(n_tracks-npositrons);
myhist.nClustCand.Fill(hps_event.getNumberOfEcalClusters())
# if(nPassTrkCuts>0):
myhist.saveHistograms(output_file)
print "******************************************************************************************"
print "Number of Events:\t\t",nEvents,"\t\t\t",float(nEvents)/nEvents,"\t\t\t",float(nEvents)/nEvents
print "N(particle) Cuts:\t\t",nPassBasicCuts,"\t\t\t",float(nPassBasicCuts)/nEvents,"\t\t\t",float(nPassBasicCuts)/nEvents
print "V0 Vertex Cuts:\t\t",nPassV0Cuts,"\t\t\t",float(nPassV0Cuts)/nPassBasicCuts,"\t\t\t",float(nPassV0Cuts)/nEvents
print "Tracking Cuts:\t\t",nPassTrkCuts,"\t\t\t",float(nPassTrkCuts)/nPassV0Cuts,"\t\t\t",float(nPassTrkCuts)/nEvents
print "ECal Match Cuts:\t\t",nPassECalMatch,"\t\t\t",float(nPassECalMatch)/nPassTrkCuts,"\t\t\t",float(nPassECalMatch)/nEvents
print "Number of Fake Events Added: \t\t",nFakeTri,"\t\t\t",float(nFakeTri)/nPassECalMatch
def morph(hist1, hist2, mass1, mass2, targetMass, debug=False):
from ROOT import gROOT, RooRealVar, RooIntegralMorph,\
kRed,kBlue,kViolet, RooDataHist, RooArgSet,\
RooArgList, RooHistPdf, RooAbsReal, RooFit
import re
if mass1 < mass2:
histLow = hist1
massLow = mass1
histHigh = hist2
massHigh = mass2
else:
histLow = hist2
massLow = mass2
histHigh = hist1
massHigh = mass1
x = RooRealVar("x", "x",
histLow.GetXaxis().GetXmin(),
histLow.GetXaxis().GetXmax())
mAlpha = 1.0 - float(targetMass - massLow) / float(massHigh - massLow)
print 'alpha:', mAlpha,
newIntegral = histLow.Integral() + \
(targetMass - massLow) * \
(histHigh.Integral()-histLow.Integral()) / \
(massHigh-massLow)
print 'low integral:', histLow.Integral(), \
'high integral:', histHigh.Integral(), \
'new integral:', newIntegral
newHistLow = RooDataHist("sigHistLow_hist", "sigHistLow_hist",
RooArgList(x), histLow)
sigHistLow = RooHistPdf("sigHistLow", "sigHistLow", RooArgList(x),
RooArgList(x), newHistLow)
newHistHigh = RooDataHist("sigHistHigh_hist", "sigHistHigh_hist",
RooArgList(x), histHigh)
sigHistHigh = RooHistPdf("sigHistHigh", "sigHistHigh", RooArgList(x),
RooArgList(x), newHistHigh)
#x.Print("v")
x.setBins(histLow.GetNbinsX(), "cache")
alpha_morph = RooRealVar("alpha_morph", "#alpha_{morph}", mAlpha, 0., 1.)
alpha_morph.setBins(10, "cache")
sigModel = RooIntegralMorph("sigModel", "sigModel", sigHistLow,
sigHistHigh, x, alpha_morph)
if (debug):
from ROOT import gPad, RooFit
frame = x.frame()
sigHistLow.plotOn(frame, RooFit.LineColor(kRed + 1),
RooFit.LineStyle(2))
sigHistHigh.plotOn(frame, RooFit.LineColor(kBlue + 1),
RooFit.LineStyle(2))
sigModel.plotOn(frame, RooFit.LineColor(kViolet + 1),
RooFit.LineStyle(9))
frame.Draw()
gPad.Update()
gPad.WaitPrimitive()
morphHist = sigModel.createHistogram(
re.sub(r'\d+', '%i' % targetMass, hist1.GetName()), x,
RooFit.Binning(histLow.GetNbinsX()))
morphHist.Scale(newIntegral / morphHist.Integral())
morphHist.SetName(re.sub(r'\d+', '%i' % targetMass, hist1.GetName()))
# if debug:
# morphHist.Print()
return morphHist
frame.Draw()
parsSig = signal.getParameters(roohistSig)
parsSig.setAttribAll('Constant', True)
if histpdfSig:
# -----------------------------------------
# hist pdf signal
canSname = 'can_Mjj' + str(mass)
canS = TCanvas(canSname, canSname, 900, 600)
gPad.SetLogy()
roohistSig = RooDataHist('roohist', 'roohist', RooArgList(x), hSig)
roohistSig.Print()
signal = RooHistPdf('signal', 'signal', RooArgSet(x), roohistSig)
signal.Print()
frame = x.frame()
roohistSig.plotOn(frame, RooFit.Binning(166))
signal.plotOn(frame, RooFit.Binning(166), RooFit.LineColor(ROOT.kRed),
RooFit.LineWidth(2), RooFit.LineStyle(ROOT.kDashed))
#frame.GetXaxis().SetRangeUser(1118,6099)
frame.GetXaxis().SetRangeUser(minX_mass, maxX_mass)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
# parsSig = signal.getParameters(roohistSig)
# parsSig.setAttribAll('Constant', True)
if fitDat:
def main(options, args):
from ROOT import gSystem, gROOT, gStyle
gROOT.SetBatch()
gSystem.Load("libRooFitCore")
if options.doWebPage:
from lip.Tools.rootutils import loadToolsLib, apply_modifs
loadToolsLib()
from ROOT import TFile, RooFit, RooArgSet, RooDataHist, RooKeysPdf, RooHistPdf, TCanvas, TLegend, TLatex, TArrow, TPaveText, RooAddPdf, RooArgList
from ROOT import kWhite, kBlue, kOpenSquare
if options.doWebPage:
from ROOT import HtmlHelper, HtmlTag, HtmlTable, HtmlPlot
rootglobestyle.setTDRStyle()
gStyle.SetMarkerSize(1.5)
gStyle.SetTitleYOffset(1.5)
gStyle.SetPadLeftMargin(0.16)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.13)
gStyle.SetLabelFont(42, "XYZ")
gStyle.SetLabelOffset(0.007, "XYZ")
gStyle.SetLabelSize(0.05, "XYZ")
gStyle.SetTitleSize(0.06, "XYZ")
gStyle.SetTitleXOffset(0.9)
gStyle.SetTitleYOffset(1.24)
gStyle.SetTitleFont(42, "XYZ")
##
## Read files
##
options.outdir = "%s_m%1.0f" % (options.outdir, options.mH)
if options.fp:
options.outdir += "_fp"
ncat = options.ncat
cats = options.cats
if cats is "":
categories = ["_cat%d" % i for i in range(0, ncat)]
else:
categories = ["_cat%s" % i for i in cats.split(",")]
if options.mva:
clables = {
"_cat0": ("MVA > 0.89", ""),
"_cat1": ("0.74 #leq MVA", "MVA < 0.89"),
"_cat2": ("0.545 #leq MVA", "MVA < 0.74"),
"_cat3": ("0.05 #leq MVA", "MVA < 0.545"),
"_cat4": ("Di-jet", "Tagged"),
"_cat5": ("Di-jet", "Tagged"),
"_combcat": ("All Classes", "Combined")
}
else:
clables = {
"_cat0": ("max(|#eta|<1.5", "min(R_{9})>0.94"),
"_cat1": ("max(|#eta|<1.5", "min(R_{9})<0.94"),
"_cat2": ("max(|#eta|>1.5", "min(R_{9})>0.94"),
"_cat3": ("max(|#eta|>1.5", "min(R_{9})<0.94"),
"_cat4": ("Di-jet", "Tagged"),
"_cat5": ("Di-jet", "Tagged"),
"_combcat": ("All Classes", "Combined")
}
helper = Helper()
fin = TFile.Open(options.infile)
helper.files.append(fin)
ws = fin.Get("cms_hgg_workspace")
mass = ws.var("CMS_hgg_mass")
mass.SetTitle("m_{#gamma#gamma}")
mass.setUnit("GeV")
mass.setRange(100., 150.)
mass.setBins(100, "plot")
mass.setBins(5000)
print ws
aset = RooArgSet(mass)
helper.objs.append(mass)
helper.objs.append(aset)
fitopt = (RooFit.Minimizer("Minuit2", ""), RooFit.Minos(False),
RooFit.SumW2Error(False), RooFit.NumCPU(8))
if not options.binned and not options.refit:
finpdf = TFile.Open(options.infilepdf)
helper.files.append(finpdf)
wspdf = finpdf.Get("wsig")
else:
wspdf = ws
for c in categories:
processes = ["ggh", "vbf", "wzh"]
if options.fp:
processes = ["vbf", "wzh"]
### elif clables[c][0] == "Di-jet":
### processes = [ "vbf", "ggh" ]
dsname = "sig_mass_m%1.0f%s" % (options.mH, c)
print dsname
print ws
ds = ws.data("sig_%s_mass_m%1.0f%s" %
(processes[0], options.mH, c)).Clone(dsname)
for proc in processes[1:]:
ds.append(ws.data("sig_%s_mass_m%1.0f%s" % (proc, options.mH, c)))
helper.dsets.append(ds)
if options.binned:
binned_ds = RooDataHist("binned_%s" % dsname, "binned_%s" % dsname,
aset, ds)
pdf = RooKeysPdf("pdf_%s_%s" % (dsname, f), "pdf_%s" % dsname,
mass, ds)
plot_pdf = RooHistPdf("pdf_%s" % dsname, "pdf_%s" % dsname, aset,
plot_ds)
helper.add(binned_ds, binned_ds.GetName())
else:
if options.refit:
if options.refitall and clables[c][0] != "Di-jet":
rpdfs = []
for proc in processes:
for ngaus in range(1, 4):
pp = build_pdf(ws, "%s_%s" % (c, proc), ngaus,
ngaus == 3)
pp.fitTo(
ws.data("sig_%s_mass_m%1.0f%s" %
(proc, options.mH, c)),
RooFit.Strategy(0), *fitopt)
rpdfs.append(pp)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c,
RooArgList(*tuple(rpdfs)))
else:
if options.refitall and clables[c][0] == "Di-jet":
for ngaus in range(1, 5):
pdf = build_pdf(ws, c, ngaus, ngaus == 5)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt)
else:
for ngaus in range(1, 4):
pdf = build_pdf(ws, c, ngaus, ngaus == 3)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt)
else:
pdfs = (wspdf.pdf("hggpdfrel%s_%s" % (c, p))
for p in processes)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c,
RooArgList(*pdfs))
helper.add(pdf, pdf.GetName())
plot_pdf = pdf.Clone("pdf_%s" % dsname)
plot_ds = RooDataHist("plot_%s" % dsname, "plot_%s" % dsname, aset,
"plot")
plot_ds.add(ds)
cdf = pdf.createCdf(aset)
hmin, hmax, hm = get_FWHM(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
wmin, wmax = get_eff_sigma(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
### hmin, hmax, hm = get_FWHM( points )
helper.add(plot_ds, plot_ds.GetName())
helper.add(plot_pdf, plot_pdf.GetName())
helper.add((wmin, wmax), "eff_sigma%s" % c)
helper.add((hmin, hmax, hm), "FWHM%s" % c)
helper.add(ds.sumEntries(),
"sumEntries%s" % c) # signal model integral
# data integral for PAS tables
data = ws.data("data_mass%s" % c)
helper.add(
data.sumEntries("CMS_hgg_mass>=%1.4f && CMS_hgg_mass<=%1.4f" %
(options.mH - 10., options.mH + 10.)),
"data_sumEntries%s" % c)
del cdf
del pdf
dsname = "sig_mass_m%1.0f_combcat" % options.mH
print dsname
combined_ds = helper.dsets[0].Clone(dsname)
for d in helper.dsets[1:]:
combined_ds.append(d)
if options.binned:
binned_ds = RooDataHist("binned_%s" % dsname, "binned_%s" % dsname,
aset, combined_ds)
pdf = RooKeysPdf("pdf_%s" % (dsname), "pdf_%s" % dsname, mass,
combined_ds)
plot_pdf = RooHistPdf("pdf_%s" % dsname, "pdf_%s" % dsname, aset,
plot_ds)
helper.add(binned_ds, binned_ds.GetName())
else:
#### pdf = build_pdf(ws,"_combcat")
#### pdf.fitTo(combined_ds, RooFit.Strategy(0), *fitopt )
#### plot_pdf = pdf.Clone( "pdf_%s" % dsname )
pdf = RooAddPdf(
"pdf_%s" % dsname, "pdf_%s" % dsname,
RooArgList(*(helper.histos["hggpdfrel%s" % c]
for c in categories)))
plot_pdf = pdf
cdf = pdf.createCdf(aset)
plot_ds = RooDataHist("plot_%s" % dsname, "plot_%s" % dsname, aset, "plot")
plot_ds.add(combined_ds)
wmin, wmax = get_eff_sigma(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
hmin, hmax, hm = get_FWHM(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
helper.add(plot_ds, plot_ds.GetName())
helper.add(plot_pdf, plot_pdf.GetName())
helper.add((wmin, wmax), "eff_sigma_combcat")
helper.add((hmin, hmax, hm), "FWHM_combcat")
helper.add(plot_ds.sumEntries(), "sumEntries_combcat")
mass.setRange("higgsrange", options.mH - 25., options.mH + 15.)
del cdf
del pdf
del helper.dsets
### label = TLatex(0.1812081,0.8618881,"#scale[0.8]{#splitline{CMS preliminary}{Simulation}}")
label = TLatex(0.7, 0.86,
"#scale[0.65]{#splitline{CMS preliminary}{Simulation}}")
label.SetNDC(1)
##
## Make web page with plots
##
if options.doWebPage:
hth = HtmlHelper(options.outdir)
hth.navbar().cell(HtmlTag("a")).firstChild().txt("..").set(
"href", "../?C=M;O=D")
hth.navbar().cell(HtmlTag("a")).firstChild().txt("home").set(
"href", "./")
tab = hth.body().add(HtmlTable())
ip = 0
for c in ["_combcat"] + categories:
### for c in categories:
if options.doWebPage and ip % 4 == 0:
row = tab.row()
ip = ip + 1
dsname = "sig_mass_m%1.0f%s" % (options.mH, c)
canv = TCanvas(dsname, dsname, 600, 600)
helper.objs.append(canv)
### leg = TLegend(0.4345638,0.6835664,0.9362416,0.9178322)
leg = TLegend(0.2, 0.96, 0.5, 0.55)
#apply_modifs( leg, [("SetLineColor",kWhite),("SetFillColor",kWhite),("SetFillStyle",0),("SetLineStyle",0)] )
hplotcompint = mass.frame(RooFit.Bins(250), RooFit.Range("higgsrange"))
helper.objs.append(hplotcompint)
helper.objs.append(leg)
plot_ds = helper.histos["plot_%s" % dsname]
plot_pdf = helper.histos["pdf_%s" % dsname]
wmin, wmax = helper.histos["eff_sigma%s" % c]
hmin, hmax, hm = helper.histos["FWHM%s" % c]
print hmin, hmax, hm
style = (RooFit.LineColor(kBlue), RooFit.LineWidth(2),
RooFit.FillStyle(0))
style_seff = (
RooFit.LineWidth(2),
RooFit.FillStyle(1001),
RooFit.VLines(),
RooFit.LineColor(15),
)
style_ds = (RooFit.MarkerStyle(kOpenSquare), )
plot_ds.plotOn(hplotcompint, RooFit.Invisible())
plot_pdf.plotOn(hplotcompint, RooFit.NormRange("higgsrange"),
RooFit.Range(wmin, wmax), RooFit.FillColor(19),
RooFit.DrawOption("F"), *style_seff)
seffleg = hplotcompint.getObject(int(hplotcompint.numItems() - 1))
plot_pdf.plotOn(hplotcompint, RooFit.NormRange("higgsrange"),
RooFit.Range(wmin, wmax), RooFit.LineColor(15),
*style_seff)
plot_pdf.plotOn(hplotcompint, RooFit.NormRange("higgsrange"),
RooFit.Range("higgsrange"), *style)
pdfleg = hplotcompint.getObject(int(hplotcompint.numItems() - 1))
plot_ds.plotOn(hplotcompint, *style_ds)
pointsleg = hplotcompint.getObject(int(hplotcompint.numItems() - 1))
iob = int(hplotcompint.numItems() - 1)
leg.AddEntry(pointsleg, "Simulation", "pe")
leg.AddEntry(pdfleg, "Parametric model", "l")
leg.AddEntry(seffleg,
"#sigma_{eff} = %1.2f GeV " % (0.5 * (wmax - wmin)), "fl")
clabel = TLatex(0.74, 0.65,
"#scale[0.65]{#splitline{%s}{%s}}" % clables[c])
clabel.SetNDC(1)
helper.objs.append(clabel)
hm = hplotcompint.GetMaximum() * 0.5 * 0.9
### hm = pdfleg.GetMaximum()*0.5
fwhmarrow = TArrow(hmin, hm, hmax, hm)
fwhmarrow.SetArrowSize(0.03)
helper.objs.append(fwhmarrow)
fwhmlabel = TPaveText(0.20, 0.58, 0.56, 0.48, "brNDC")
fwhmlabel.SetFillStyle(0)
fwhmlabel.SetLineColor(kWhite)
reducedFWHM = (hmax - hmin) / 2.3548200
fwhmlabel.AddText("FWHM/2.35 = %1.2f GeV" % reducedFWHM)
helper.objs.append(fwhmlabel)
hplotcompint.SetTitle("")
hplotcompint.GetXaxis().SetNoExponent(True)
hplotcompint.GetXaxis().SetTitle("m_{#gamma#gamma} (GeV)")
hplotcompint.GetXaxis().SetNdivisions(509)
## hplotcompint.GetYaxis().SetTitle("A.U.");
## hplotcompint.GetYaxis().SetRangeUser(0.,hplotcompint.GetMaximum()*1.4);
hplotcompint.Draw()
leg.Draw("same")
label.Draw("same")
clabel.Draw("same")
fwhmarrow.Draw("<>")
fwhmlabel.Draw("same")
plot_ds.sumEntries()
if options.doWebPage:
hpl = HtmlPlot(canv, False, "", True, True, True)
hpl.caption("<i>%s</i>" % canv.GetTitle())
row.cell(hpl)
else:
if os.path.isdir(options.outdir) is False:
os.mkdir(options.outdir)
for ext in "C", "png", "pdf":
canv.SaveAs(
os.path.join(options.outdir,
"%s.%s" % (canv.GetName(), ext)))
if "comb" in c:
ip = 0
if options.doWebPage:
print "Creating pages..."
hth.dump()
for f in helper.files:
f.Close()
gROOT.Reset()
from pprint import pprint
pprint(helper)
print 'Summary statistics per event class'
print 'Cat\tSignal\t\tData/GeV (in %3.1f+/-10)\tsigEff\tFWHM/2.35' % options.mH
sigTotal = 0.
dataTotal = 0.
for c in categories:
sigVal = helper.histos["sumEntries%s" % c]
datVal = helper.histos["data_sumEntries%s" % c]
sigTotal += sigVal
dataTotal += datVal
for c in categories:
sigVal = helper.histos["sumEntries%s" % c]
datVal = helper.histos["data_sumEntries%s" % c]
effSig = 0.5 * (helper.histos["eff_sigma%s" % c][1] -
helper.histos["eff_sigma%s" % c][0])
fwhm = (helper.histos["FWHM%s" % c][1] -
helper.histos["FWHM%s" % c][0]) / 2.3548200
print c, '\t%3.1f (%3.1f%%)\t%3.1f (%3.1f%%)\t\t\t%2.2f\t%2.2f' % (
sigVal, 100. * sigVal / sigTotal, datVal /
(10. + 10.), 100. * datVal / dataTotal, effSig, fwhm)
print "Done."
#eff_p4= acceptanceFile.Get("l1_p4").GetFunction("pol4").Eval(mass)
eff_p4 = 0
exppol4.SetParameters(eff_p0, eff_p1, eff_p2, eff_p3, eff_p4)
exppol4.Draw()
exppol4.GetYaxis().SetRangeUser(0, 2)
fitfunc.SetParameters(1.0, mean, sigma, breakz, length)
bkgHist.Reset()
for binnum in xrange(1, nbins_histpdf + 1):
bkgHist.SetBinContent(
binnum, fitfunc.Eval(bkgHist.GetXaxis().GetBinCenter(binnum)))
# bkgHist.Draw()
bkgDataHist = RooDataHist("bkgDataHist_{0}".format(i),
"bkgDataHist_{0}".format(i),
RooArgList(w.set("obs_1d")), bkgHist)
bkgPdf = RooHistPdf("bkgPdf_{0}".format(i), "bkgPdf_{0}".format(i),
w.set("obs_1d"), bkgDataHist)
getattr(w, 'import')(bkgPdf, RooFit.Silence())
#w.factory("Exponential::bkg_{0}(uncVZ,{0})".format(i,-1.0/length))
#w.factory("EXPR::gaussExp_{0}('exp( ((@0-{1})<{3})*(-0.5*(@0-{1})^2/{2}^2) + ((@0-{1})>={3})*(-0.5*{3}^2/{2}^2-(@0-{1}-{3})/{4}))',uncVZ)".format(i,mean,sigma,breakz,length))
c.Clear()
c.SetLogy()
vtxZRefitUnc.setRange("fitRange", tailcut, 50)
frame = vtxZRefitUnc.frame()
frame.SetAxisRange(tailcut, 50)
frame.SetTitle(name)
frame.GetXaxis().SetTitle("Vertex Z [mm]")
dataPastCut2.plotOn(frame)
thispdf = w.pdf("bkgPdf_{0}".format(i))
fitresult = thispdf.fitTo(dataPastCut2, RooFit.Range("fitRange"),
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(
description=
'Script that creates combine datacards and corresponding RooFit workspaces',
epilog=usage)
parser.add_argument("analysis", type=str, help="Analysis name")
parser.add_argument("model", type=str, help="Model (Hbb, RSG)")
#parser.add_argument("--inputData", dest="inputData", required=True,
# help="Input data spectrum",
# metavar="INPUT_DATA")
parser.add_argument("--dataHistname",
dest="dataHistname",
type=str,
default="h_data",
help="Data histogram name",
metavar="DATA_HISTNAME")
#parser.add_argument("--inputSig", dest="inputSig", required=True,
# help="Input signal shapes",
# metavar="INPUT_SIGNAL")
parser.add_argument("-f",
"--final_state",
dest="final_state",
default="qq",
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--fit_functions",
dest="fit_functions",
default="f1,f2,f3,f4,f5",
help="List of fit functions")
#parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument(
"-o",
"--output_path",
dest="output_path",
help=
"Output path where datacards and workspaces will be stored. If not specified, this is derived from limit_configuration.",
metavar="OUTPUT_PATH")
parser.add_argument("--correctTrigger",
dest="correctTrigger",
action='store_true',
help="Include trigger correction in PDF")
parser.add_argument(
"-l",
"--lumi",
dest="lumi",
default=19700.,
type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument(
"--massMin",
dest="massMin",
default=500,
type=int,
help=
"Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument(
"--massMax",
dest="massMax",
default=1200,
type=int,
help=
"Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument(
"--fitSignal",
action="store_true",
help=
"Use signal fitted shapes (CB+Voigtian) instead of histogram templates"
)
#parser.add_argument("--lumiUnc", dest="lumiUnc",
# required=True, type=float,
# help="Relative uncertainty in the integrated luminosity",
# metavar="LUMI_UNC")
#parser.add_argument("--jesUnc", dest="jesUnc",
# type=float,
# help="Relative uncertainty in the jet energy scale",
# metavar="JES_UNC")
#parser.add_argument("--jerUnc", dest="jerUnc",
# type=float,
# help="Relative uncertainty in the jet energy resolution",
# metavar="JER_UNC")
parser.add_argument(
"--sqrtS",
dest="sqrtS",
default=8000.,
type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3",
dest="fixP3",
default=False,
action="store_true",
help="Fix the fit function p3 parameter")
parser.add_argument("--runFit",
dest="runFit",
default=False,
action="store_true",
help="Run the fit")
parser.add_argument("--fitBonly",
dest="fitBonly",
default=False,
action="store_true",
help="Run B-only fit")
parser.add_argument("--fixBkg",
dest="fixBkg",
default=False,
action="store_true",
help="Fix all background parameters")
parser.add_argument("--decoBkg",
dest="decoBkg",
default=False,
action="store_true",
help="Decorrelate background parameters")
parser.add_argument("--fitStrategy",
dest="fitStrategy",
type=int,
default=1,
help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug",
dest="debug",
default=False,
action="store_true",
help="Debug printout")
parser.add_argument(
"--postfix",
dest="postfix",
default='',
help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes",
dest="pyes",
default=False,
action="store_true",
help="Make files for plots")
parser.add_argument("--jyes",
dest="jyes",
default=False,
action="store_true",
help="Make files for JES/JER plots")
parser.add_argument(
"--pdir",
dest="pdir",
default='testarea',
help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2",
dest="chi2",
default=False,
action="store_true",
help="Compute chi squared")
parser.add_argument("--widefit",
dest="widefit",
default=False,
action="store_true",
help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument(
"--mass",
type=int,
nargs='*',
default=1000,
help=
"Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument(
"--massrange",
type=int,
nargs=3,
help="Define a range of masses to be produced. Format: min max step",
metavar=('MIN', 'MAX', 'STEP'))
mass_group.add_argument("--masslist",
help="List containing mass information")
args = parser.parse_args()
fit_functions = args.fit_functions.split(",")
# mass points for which resonance shapes will be produced
masses = []
if args.fitBonly:
masses.append(750)
else:
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX + STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py", ""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
#inputData = TFile(limit_config.get_data_input(args.analysis))
# input data histogram
#hData = inputData.Get(args.dataHistname)
#hData.SetDirectory(0)
data_file = TFile(
analysis_config.get_b_histogram_filename(args.analysis,
"BJetPlusX_2012"))
hData = data_file.Get("BHistograms/h_pfjet_mjj")
hData.SetDirectory(0)
# input sig file
if not args.fitSignal:
print "[create_datacards] INFO : Opening resonance shapes file at " + limit_config.get_resonance_shapes(
args.analysis, args.model)
inputSig = TFile(
limit_config.get_resonance_shapes(args.analysis, args.model),
"READ")
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.massMin), float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
if args.correctTrigger:
trigger_efficiency_pdf = trigger_efficiency.get_pdf(args.analysis, mjj)
trigger_efficiency_formula = trigger_efficiency.get_formula(
args.analysis, mjj)
else:
trigger_efficiency_pdf = trigger_efficiency.get_trivial_pdf(mjj)
trigger_efficiency_formula = trigger_efficiency.get_trivial_formula(
mjj)
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.final_state, int(mass))
rooDataHist = RooDataHist('rooDatahist', 'rooDathist', RooArgList(mjj),
hData)
if not args.fitSignal:
hSig = inputSig.Get("h_" + args.final_state + "_" + str(int(mass)))
if not hSig:
raise Exception("Couldn't find histogram " + "h_" +
args.final_state + "_" + str(int(mass)) +
" in file " +
limit_config.get_resonance_shapes(
args.analysis, args.model))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist',
RooArgList(mjj), hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries() /
hSig.Integral())
# If using fitted signal shapes, load the signal PDF
if args.fitSignal:
print "[create_datacards] Loading fitted signal PDFs from " + analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(not mass
in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(
analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses)),
"READ")
w_signal = f_signal_pdfs.Get("w_signal")
input_parameters = signal_fits.get_parameters(
w_signal.pdf("signal"))
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic(
"bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
#signal_pdf = RooProdPdf(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_pdf)
signal_pdf = RooEffProd(signal_pdf_name, signal_pdf_name,
signal_pdf_notrig,
trigger_efficiency_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_parameters["xp"][0])
signal_vars["xp_0"].setError(input_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_parameters["xi"][0])
signal_vars["xi_0"].setError(input_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(
fit_function)
if args.fitSignal:
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(
signal_pdf_notrig,
signal_pdf_notrig.GetName() + "_" + fit_function)
signal_pdfs[fit_function] = RooEffProd(
signal_pdf.GetName() + "_" + fit_function,
signal_pdf.GetName() + "_" + fit_function,
signal_pdfs_notrig[fit_function],
trigger_efficiency_formula)
#signal_pdfs[fit_function] = RooProdPdf(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_pdf)
iterator = signal_pdfs_notrig[fit_function].getVariables(
).createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
else:
signal_pdfs[fit_function] = RooHistPdf(
'signal_' + fit_function, 'signal_' + fit_function,
RooArgSet(mjj), rooSigHist)
signal_norms[fit_function] = RooRealVar(
'signal_norm_' + fit_function, 'signal_norm_' + fit_function,
0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
background_pdfs_notrig[fit_function], background_parameters[
fit_function] = make_background_pdf(fit_function,
mjj,
collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName(
)
background_pdfs_notrig[fit_function].SetName(background_pdf_name +
"_notrig")
background_pdfs[fit_function] = RooEffProd(
background_pdf_name, background_pdf_name,
background_pdfs_notrig[fit_function],
trigger_efficiency_formula)
#background_pdfs[fit_function] = RooProdPdf(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_pdf)
#background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
#background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(55.)
background_parameters[fit_function]["p1"].setMin(20.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(28.)
background_parameters[fit_function]["p2"].setVal(-22.)
background_parameters[fit_function]["p3"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(82.)
background_parameters[fit_function]["p1"].setMin(60.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(41.)
background_parameters[fit_function]["p2"].setVal(-45.)
background_parameters[fit_function]["p3"].setVal(10.)
data_integral = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar(
'background_' + fit_function + '_norm',
'background_' + fit_function + '_norm', data_integral, 0.,
1.e8)
signal_epdfs[fit_function] = RooExtendPdf(
'esignal_' + fit_function, 'esignal_' + fit_function,
signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf(
'ebackground_' + fit_function, 'ebackground_' + fit_function,
background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf(
'model_' + fit_function, 's+b',
RooArgList(background_epdfs[fit_function],
signal_epdfs[fit_function]))
if args.runFit:
print "[create_datacards] INFO : Starting fit with function {}".format(
fit_function)
fit_results[fit_function] = models[fit_function].fitTo(
rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(
fit_function)
fit_results[fit_function].Print()
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[
fit_function].iteritems():
par.setConstant()
# -----------------------------------------
#signal_pdfs_syst = {}
# JES and JER uncertainties
if args.fitSignal:
print "[create_datacards] INFO : Getting signal PDFs from " + analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(not mass
in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(
analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses)))
w_signal = f_signal_pdfs.Get("w_signal")
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
#print w_signal.pdf("signal__JESUp")
#print signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))
xp_up = signal_fits.get_parameters(
w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(
w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(
max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(
signal_vars["dxp"].getVal(), xp_down, xp_central,
xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(
w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(
w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(
max(abs(sigp_up - sigp_central),
abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
#for variation in ["JERUp", "JERDown"]:
# signal_pdfs_syst[variation] = w_signal.pdf("signal__" + variation)
#for variation, pdf in signal_pdfs_syst.iteritems():
# signal_parameters = pdf.getVariables()
# iter = signal_parameters.createIterator()
# var = iter.Next()
# while var:
# var.setConstant()
# var = iter.Next()
f_signal_pdfs.Close()
else:
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX() + 1)
if "jes" in systematics or "jer" in systematics:
sigCDF.SetPoint(0, 0., 0.)
integral = 0.
for i in range(1, hSig.GetNbinsX() + 1):
x = hSig.GetXaxis().GetBinLowEdge(i + 1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i, x, integral)
if "jes" in systematics:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if "jer" in systematics:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if "jes" in systematics:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jes = 1. - systematics["jes"]
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + systematics["jes"]
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist(
'hSig_JESUp', 'hSig_JESUp', RooArgList(mjj),
hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist(
'hSig_JESDown', 'hSig_JESDown', RooArgList(mjj),
hSig_Syst['JESDown'])
# produce JER signal shapes
if "jer" in systematics:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jer = 1. - systematics["jer"]
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + systematics["jer"]
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist(
'hSig_JERUp', 'hSig_JERUp', RooArgList(mjj),
hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist(
'hSig_JERDown', 'hSig_JERDown', RooArgList(mjj),
hSig_Syst['JERDown'])
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(
mass) + postfix + '.root'
w = RooWorkspace('w', 'workspace')
if args.fitSignal:
signal_pdf.SetName("signal")
getattr(w, 'import')(signal_pdf, RooFit.Rename("signal"))
# Create a norm variable "signal_norm" which normalizes the PDF to unity.
norm = args.lumi
#signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", 1. / norm, 0.1 / norm, 10. / norm)
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm",
norm / 100., norm / 100. / 10.,
norm * 10.)
#else:
# signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm, norm / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(
signal_norm.getVal())
signal_norm.setConstant()
getattr(w, 'import')(signal_norm, ROOT.RooCmdArg())
#if "jes" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JESUp'],RooFit.Rename("signal__JESUp"))
# getattr(w,'import')(signal_pdfs_syst['JESDown'],RooFit.Rename("signal__JESDown"))
#if "jer" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JERUp'],RooFit.Rename("signal__JERUp"))
# getattr(w,'import')(signal_pdfs_syst['JERDown'],RooFit.Rename("signal__JERDown"))
else:
getattr(w, 'import')(rooSigHist, RooFit.Rename("signal"))
if "jes" in systematics:
getattr(w, 'import')(hSig_Syst_DataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if "jer" in systematics:
getattr(w, 'import')(hSig_Syst_DataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w, 'import')(background_deco, ROOT.RooCmdArg())
else:
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,
'import')(background_pdfs[fit_function],
ROOT.RooCmdArg(),
RooFit.Rename("background_" + fit_function),
RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w, 'import')(background_norms[fit_function],
ROOT.RooCmdArg(),
RooFit.Rename("background_" +
fit_function + "_norm"))
getattr(w, 'import')(fit_results[fit_function])
getattr(w, 'import')(signal_norms[fit_function],
ROOT.RooCmdArg())
if args.fitBonly:
getattr(w, 'import')(models[fit_function],
ROOT.RooCmdArg(),
RooFit.RecycleConflictNodes())
getattr(w, 'import')(rooDataHist, RooFit.Rename("data_obs"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir(os.path.join(os.getcwd(), args.output_path)):
os.mkdir(os.path.join(os.getcwd(), args.output_path))
print "[create_datacards] INFO : Writing workspace to file {}".format(
os.path.join(args.output_path, wsName))
w.writeToFile(os.path.join(args.output_path, wsName))
else:
print "[create_datacards] INFO : Writing workspace to file {}".format(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitBonly=args.fitBonly,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
w.writeToFile(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitBonly=args.fitBonly,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
if not args.fitBonly:
beffUnc = 0.3
boffUnc = 0.06
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(
mass) + postfix + '_' + fit_function + '.txt'
print "[create_datacards] INFO : Writing datacard to file {}".format(
os.path.join(args.output_path, dcName))
datacard = open(os.path.join(args.output_path, dcName),
'w')
else:
print "[create_datacards] INFO : Writing datacard to file {}".format(
limit_config.get_datacard_filename(
args.analysis,
args.model,
mass,
fit_function,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
datacard = open(
limit_config.get_datacard_filename(
args.analysis,
args.model,
mass,
fit_function,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if ("jes" in systematics
or "jer" in systematics) and not args.fitSignal:
if args.output_path:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * ' + os.path.basename(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger)) +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
if args.output_path:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS\n')
else:
datacard.write('shapes * * ' + os.path.basename(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger)) +
' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background_' +
fit_function + '\n')
datacard.write('process 0 1\n')
if args.fitSignal:
datacard.write('rate 1 1\n')
else:
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n' %
(1. + systematics["luminosity"]))
datacard.write('beff lnN %f -\n' % (1. + beffUnc))
datacard.write('boff lnN %f -\n' % (1. + boffUnc))
#datacard.write('bkg lnN - 1.03\n')
if args.fitSignal:
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
else:
if "jes" in systematics:
datacard.write('JES shape 1 -\n')
if "jer" in systematics:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_' + fit_function +
'_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
else:
for par_name, par in background_parameters[
fit_function].iteritems():
datacard.write(fit_function + "_" + par_name +
' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "All done."
